From 7fde0c299a0fdd7425023af2283b88bfcbc880b7 Mon Sep 17 00:00:00 2001 From: Shane Smiskol Date: Mon, 14 Sep 2020 14:24:04 -0500 Subject: [PATCH 1/2] Bring curv-learner up to date with latest SA (#222) * Add curvature learner atop latest stock_additions * Clean up curvature learner, define values in dicts * does this add or replace? * add more of my data * new tune, branch should be working now * rearrange * Up write freq * new tune, increase lr, and use curvature from start of path not car * tune curvature bands thanks to trae! slightly less variance * make abs * this was most common for outer * add kmeans clustering test file * update * fix all zeros * add more data from trae! * clean up cluster_curvature * add version and test file * fix scaling factor * add new clustering to curvature_learner.py * rewrite file if version is newer than current version * debugging * temp fix * temp fix * fix * revert, ready to test! * add more of trae's data * multiply cluster y coords by factor, don't need to keep doing it every loop * rename * add timing * measure one second * measure one second * measure one second * revert * use list instead of dict * switching to list gave us about 5% speed increase! some clean up * lower min lr prob * clean up * speed up learning rate * refactor * clean up * remove old file * Revert "remove old file" This reverts commit aff8cec737b3982c22b4487ed557e54785d5c884. * Add more data! * refactor cluster_curvature a bit * 1 more cluster for low speed, more y weight * update version and clusters * Revert INDI Prius Values (#163) * add Curvature Learner to readme * update readme * add cluster image * fix * fix * fix * center * Update README.md * Update README.md * update intro text! * add more info to features section * update wording * Add fast learning. each cluster in each direction gets ~2.3 minutes of 5x learning speed * bump version * create a folder for curvature_learner * remove old file * remove old file * lower learning rate and multiplier * tune clusters and fast learning rate * add more data! * some clean up and comments * only calculate abs(lat_pos) once, move direction calc to cluster_sample * move function under update * bump version * remove comment * one less cluster * bump version * 30 -> 20 min * bump version * tune clusters and 90 seconds per cluster. up min curvature * fix y axis factor * separate file to keep the offset file clean, add a param to toggle curv-learner on and off * tune clusters and add cluster names back, strip leading and trailing 0's * whoops * another fix! * back to known good * add distplot for min_angle as a curvature * Force battery temperature to 0 on comma two (#165) * copy with same name as old just to diffs are easier * revert some settings to August 11th * revert fast learning rate for now * faster lr * comment out fast learning for now * switch in curvature learner reverted to be similar to August 11th * bump version * move out * remove for now * add a file to verify my logic is good * tune clusters and y axis factor * slower learning * fully revert to original learning rate * tune clusters and debug * bump version * debug * debug * Debug * Temp * add debugging options * Make live * Tune * add straight line data * remove * some param tuning for cluster_curvature.py * add center band/cluster. cluster will never be None * bump version * Disable center * Lower learning rate * remove center cluster * move lr_prob back * revert * revert * ensure float * use old planner code to calculate true curvature of poly * don't eval poly for direction detection and enable debugging * remove debugging * remove * fix * Tune * Tune * add direction * the logic is correct, idk why it's oscillating * remove lr_prob restriction to test * just so it's easier to read while debugging. positive values make it steer more! * use angle_steers since that will reduce likelihood of mistakes in calculating direction from curvature * fix * fix * revert readme * revert opparams * revert indi tuning Co-authored-by: d412k5t412 <36134180+d412k5t412@users.noreply.github.com> --- .media/curvature-clusters.png | Bin 0 -> 80946 bytes selfdrive/controls/lib/curvature_learner.py | 144 + .../cl_data/curv_learner_data-shane-4 | 29113 ++++++ .../cl_data/curv_learner_data-shane.py | 80362 ++++++++++++++++ .../cl_data/curv_learner_data-shane2 | 33597 +++++++ .../cl_data/curv_learner_data-shane3 | 19010 ++++ .../cl_data/curv_learner_data-trae | 28550 ++++++ .../cl_data/curv_learner_data-trae2 | 10045 ++ .../cl_data/curv_learner_data-trae3 | 15608 +++ .../cl_data/curv_learner_data-trae4 | 16246 ++++ .../cl_data/curv_learner_data-trae5 | 19888 ++++ .../cl_data/curv_learner_data-trae6 | 5877 ++ .../cl_data/curv_learner_data-trae7 | 16851 ++++ .../cl_data/curv_learner_data-trae8 | 12460 +++ .../cl_data/straight_line_curv_learner_data | 3342 + .../cl_data/straight_line_debugging.py | 70 + .../curvature_learner/cluster_curvature.py | 144 + .../lib/curvature_learner/cluster_lat_pos.py | 135 + .../compare_zorrobytes_learner.py | 37 + .../curvature_learner_latest_not_working.py | 134 + .../map-angle-to-curvature.py | 136 + .../curvature_learner/map-angle-to-lat-pos.py | 126 + selfdrive/controls/lib/pathplanner.py | 10 +- selfdrive/manager.py | 2 +- 24 files changed, 291885 insertions(+), 2 deletions(-) create mode 100644 .media/curvature-clusters.png create mode 100644 selfdrive/controls/lib/curvature_learner.py create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-shane-4 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-shane.py create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-shane2 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-shane3 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae2 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae3 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae4 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae5 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae6 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae7 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-trae8 create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/straight_line_curv_learner_data create mode 100644 selfdrive/controls/lib/curvature_learner/cl_data/straight_line_debugging.py create mode 100644 selfdrive/controls/lib/curvature_learner/cluster_curvature.py create mode 100644 selfdrive/controls/lib/curvature_learner/cluster_lat_pos.py create mode 100644 selfdrive/controls/lib/curvature_learner/compare_zorrobytes_learner.py create 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import Conversions as CV +from common.op_params import opParams + +# CurvatureLearner v4 by Zorrobyte +# Modified to add direction as a learning factor as well as clusters based on speed x curvature (lateral pos in 0.9 seconds) +# Clusters found with sklearn KMeans, this assigns more clusters to areas with more data and viceversa +# Version 5 due to json incompatibilities + +GATHER_DATA = True +VERSION = 5.83 + +FT_TO_M = 0.3048 + + +def find_distance(pt1, pt2): + x1, x2 = pt1[0], pt2[0] + y1, y2 = pt1[1], pt2[1] + return math.hypot(x2 - x1, y2 - y1) + + +# def eval_poly(poly, x): +# return poly[2]*x + poly[1]*x**2 + poly[0]*x**3 + + +class CurvatureLearner: + def __init__(self): + self.op_params = opParams() + self.curvature_file = '/data/curvature_offsets.json' + # self.fast_learn_file = '/data/curvature_fast_learn.json' + rate = 1 / 20. # pathplanner is 20 hz + # self.learning_rate = 2.5833e-3 * rate + self.learning_rate = .8e-3 * rate # equivelent to x / 12000 + self.write_frequency = 5 # in seconds + self.min_lr_prob = .5 + self.min_speed = 15 * CV.MPH_TO_MS + self.TR = 1.2 + + self.y_axis_factor = 177.32840012 # weight y/curvature as much as speed + self.min_curvature = 0.02 # from map-angle-to-curvature + + self.directions = ['left', 'right'] + self.cluster_coords = [[10.05319434, 22.75010184], [11.56311835, 4.66648626], [13.57491646, 11.64505509], [18.36658232, 2.41488393], + [18.98342402, 7.59894138], [24.24826343, 7.03329587], [25.79352542, 2.00537934], [28.80542914, 5.36122992]] + # self.cluster_names = ['CLUSTER_{}'.format(idx) for idx in range(len(self.cluster_coords))] + self.cluster_names = ['22.5MPH-.13CURV', '25.9MPH-.03CURV', '30.4MPH-.07CURV', '41.1MPH-.01CURV', '42.5MPH-.04CURV', '54.2MPH-.04CURV', '57.7MPH-.01CURV', '64.4MPH-.03CURV'] + + # self.fast_learning_for = 90 # seconds per cluster # todo: finish this once curv-learner is working well + # self.fast_learning_for = round(self.fast_learning_for / rate) # speed up learning first time user uses curvature learner + # self.fast_lr_multiplier = 2. # 2x faster learning until ~1 MIN for each cluster + self._load_curvature() + + def update(self, v_ego, d_poly, lane_probs, angle_steers): + self._gather_data(v_ego, d_poly, angle_steers) + offset = 0 + if v_ego < self.min_speed or math.isnan(d_poly[0]) or len(d_poly) != 4 or not self.op_params.get('curvature_learner'): + return offset + + cluster, direction, curvature = self.cluster_sample(v_ego, d_poly, angle_steers) + d_poly_offset = float(d_poly[3]) + if cluster is not None: + lr_prob = lane_probs[0] + lane_probs[1] - lane_probs[0] * lane_probs[1] + # if lr_prob >= self.min_lr_prob: # only learn when lane lines are present; still use existing offset + if direction == 'right': + d_poly_offset = -d_poly_offset # d_poly's sign switches for oversteering in different directions + + # lr = self.get_learning_rate(direction, cluster) # todo: faster learning for first ~minute per cluster + lr = self.learning_rate + self.learned_offsets[direction][cluster] -= d_poly_offset * self.learning_rate # the learning + offset = -self.learned_offsets[direction][cluster] + print('CLUSTER: {}\nOFFSET: {}\nCURV: {}\nDIR: {}\n-----'.format(cluster, round(offset, 6), round(curvature, 6), direction)) + + + self._write_data() + return float(clip(offset, -0.05, 0.05)) + + def cluster_sample(self, v_ego, d_poly, angle_steers): + dist = v_ego * self.TR + path_x = np.arange(int(round(dist))) # eval curvature 0.9 seconds out (doesn't include path offset, just curvature) + y_p = 3 * d_poly[0] * path_x ** 2 + 2 * d_poly[1] * path_x + d_poly[2] + y_pp = 6 * d_poly[0] * path_x + 2 * d_poly[1] + curv = y_pp / (1. + y_p ** 2) ** 1.5 + # direction = 'left' if curv[0] > 0 else 'right' # todo: [0] might be fastest, but explore mean if wrong often + direction = 'left' if angle_steers > 0 else 'right' + + curv = np.sqrt(np.abs(curv)) + curv = np.max(curv) # todo: takes maximum curvature, but could experiment with averaging. edit: mean doesn't decrease std that much, except with sharp band + + closest_cluster = None + if curv >= self.min_curvature: + sample_coord = [v_ego, curv * self.y_axis_factor] # we multiply y so that the dist function weights x and y the same + dists = [find_distance(sample_coord, cluster_coord) for cluster_coord in self.cluster_coords] # todo: remove clusters far away based on v_ego to speed this up + closest_cluster = self.cluster_names[min(range(len(dists)), key=dists.__getitem__)] + return closest_cluster, direction, curv + + # def get_learning_rate(self, direction, cluster): # todo: make sure this is correct + # lr = self.learning_rate + # fast_iter_left = self.fast_learn[direction][cluster] + # if not isinstance(fast_iter_left, str): + # if 1 <= fast_iter_left: # decrement until we reach 0 + # self.fast_learn[direction][cluster] -= 1 + # lr *= self.fast_lr_multiplier + # else: # mark done + # self.fast_learn[direction][cluster] = 'done' + # return lr + + def _gather_data(self, v_ego, d_poly, angle_steers): + if GATHER_DATA: + with open('/data/curv_learner_data', 'a') as f: + f.write('{}\n'.format({'v_ego': v_ego, 'd_poly': list(d_poly), 'angle_steers': angle_steers})) + + def _load_curvature(self): + self._last_write_time = 0 + try: + with open(self.curvature_file, 'r') as f: + self.learned_offsets = json.load(f) + # with open(self.fast_learn_file, 'r') as f: + # self.fast_learn = json.load(f) + if 'version' in self.learned_offsets and self.learned_offsets['version'] == VERSION: + return + except: + pass + # can't read file, doesn't exist, or old version + # todo: old: self.learned_offsets = {d: {c: {'offset': 0., 'fast_learn': self.fast_learning_for} for c in self.cluster_names} for d in self.directions} + self.learned_offsets = {d: {c: 0. for c in self.cluster_names} for d in self.directions} + # self.fast_learn = {d: {c: self.fast_learning_for for c in self.cluster_names} for d in self.directions} + self.learned_offsets['version'] = VERSION # update version + self._write_data() # rewrite/create new file + + def _write_data(self): + if sec_since_boot() - self._last_write_time >= self.write_frequency: + with open(self.curvature_file, 'w') as f: + f.write(json.dumps(self.learned_offsets, indent=2)) + # with open(self.fast_learn_file, 'w') as f: + # f.write(json.dumps(self.fast_learn, indent=2)) + + os.chmod(self.curvature_file, 0o777) + # os.chmod(self.fast_learn_file, 0o777) + self._last_write_time = sec_since_boot() diff --git a/selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-shane-4 b/selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-shane-4 new file mode 100644 index 00000000000000..f9e9fdce562d59 --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/cl_data/curv_learner_data-shane-4 @@ -0,0 +1,29113 @@ +{'v_ego': 18.309993743896484, 'd_poly': [2.7662198232738015e-08, -1.6934717704825954e-07, -0.004313104227580634, 0.09990221104409516], 'angle_steers': 0.7416732311248779} +{'v_ego': 18.313264846801758, 'd_poly': [2.015859233563239e-08, 1.91070739349566e-06, -0.004541046036066601, 0.09954395227779791], 'angle_steers': 0.7416732311248779} +{'v_ego': 18.312475204467773, 'd_poly': [1.1067690991373238e-08, 3.0413220042738968e-06, -0.004710060934895596, 0.09827105048582217], 'angle_steers': 0.6443371772766113} +{'v_ego': 18.309581756591797, 'd_poly': [2.625986421818126e-08, -9.255522282876969e-07, -0.0047038318022060545, 0.09656393816641555], 'angle_steers': 0.5455302000045776} +{'v_ego': 18.302343368530273, 'd_poly': [1.0695433101144604e-08, 5.307226211914037e-06, -0.005168325206491726, 0.09547637399410672], 'angle_steers': -0.15322697162628174} +{'v_ego': 18.299407958984375, 'd_poly': [3.523717307721801e-08, 3.8088593898763623e-07, -0.005017493597506338, 0.09181420684316469], 'angle_steers': -0.6463654041290283} +{'v_ego': 18.300819396972656, 'd_poly': [3.326023487183379e-08, -5.808144319664486e-06, -0.0050682961688138725, 0.08862955288025232], 'angle_steers': -0.6348721981048584} +{'v_ego': 18.3049259185791, 'd_poly': [5.649035408316639e-08, -1.2988667336230438e-05, -0.0051063083362034595, 0.08635924641310688], 'angle_steers': -0.6348721981048584} +{'v_ego': 18.305917739868164, 'd_poly': [9.652296219721503e-08, -2.364329131102188e-05, -0.0046722861938168735, 0.08621739734781522], 'angle_steers': -0.6186493635177612} +{'v_ego': 18.302082061767578, 'd_poly': [8.89547579293854e-08, -1.8598383761501225e-05, -0.004809114864814938, 0.0824497735509164], 'angle_steers': -0.6115279197692871} +{'v_ego': 18.29191780090332, 'd_poly': [7.015281434491745e-08, -1.24316005649015e-05, -0.004972313644361494, 0.07980533859538116], 'angle_steers': -0.6045314073562622} +{'v_ego': 18.281435012817383, 'd_poly': [3.4910181101382824e-08, -4.359554108772878e-06, -0.005264295575855605, 0.08252227135359422], 'angle_steers': -0.6036988496780396} +{'v_ego': 18.279531478881836, 'd_poly': [2.2388824845229177e-08, -3.144135810381622e-08, -0.004865145327142794, 0.08273299868123495], 'angle_steers': -0.6011492013931274} +{'v_ego': 18.274757385253906, 'd_poly': [-1.0219551879207637e-08, 5.345018944359625e-06, -0.004659636164435506, 0.08487090434205478], 'angle_steers': -0.7996263206005096} +{'v_ego': 18.273460388183594, 'd_poly': [-1.4770410998623452e-08, 6.351266262867182e-06, -0.004262594409575823, 0.0825222054882324], 'angle_steers': -0.8006927669048309} +{'v_ego': 18.257526397705078, 'd_poly': [-2.9755329002336014e-08, 9.143717088690663e-06, -0.003929925580807363, 0.07816774734757835], 'angle_steers': -0.7920783460140228} +{'v_ego': 18.251134872436523, 'd_poly': [-2.167609818417691e-08, 6.698468624917785e-06, -0.0037147878648877575, 0.07573425845693688], 'angle_steers': -0.7867837846279144} +{'v_ego': 18.242721557617188, 'd_poly': [-2.4502910332531737e-08, 7.421927921594192e-06, -0.0035924054370813385, 0.07806092428911668], 'angle_steers': -0.7839401662349701} +{'v_ego': 18.234580993652344, 'd_poly': [-1.3465411362313172e-08, 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-0.2973237221956142], 'angle_steers': 0.41799139976501465} +{'v_ego': 25.44134521484375, 'd_poly': [-1.2575349885251518e-08, -4.619077916328026e-06, 0.01123354422560204, -0.283730020208166], 'angle_steers': 0.435089111328125} +{'v_ego': 25.444520950317383, 'd_poly': [-4.071980025718935e-08, 2.6633692044488973e-06, 0.010650916984692532, -0.27286472504470644], 'angle_steers': 0.6427886486053467} +{'v_ego': 25.45306968688965, 'd_poly': [-6.493209008505809e-08, 6.345157780926788e-06, 0.010668417115354614, -0.26630168581442787], 'angle_steers': 0.742788553237915} +{'v_ego': 25.453285217285156, 'd_poly': [-4.247957970113167e-08, 1.4999303494886562e-06, 0.01072908025234747, -0.25883910053773435], 'angle_steers': 0.8479571342468262} +{'v_ego': 25.455337524414062, 'd_poly': [-2.004272660733209e-07, 1.786469441715686e-05, 0.0105849889530001, -0.2510487189311631], 'angle_steers': 0.9560072422027588} +{'v_ego': 25.46248435974121, 'd_poly': [-2.1437879206207595e-07, 2.1523790185491137e-05, 0.010008898367645635, -0.23809384189860114], 'angle_steers': 1.061514139175415} +{'v_ego': 25.46945571899414, 'd_poly': [-2.3672896553167872e-07, 2.0151861385273518e-05, 0.010046515309626355, -0.22558833751626955], 'angle_steers': 1.0961205959320068} +{'v_ego': 25.472700119018555, 'd_poly': [-8.867686210510059e-08, 1.7923318183772316e-06, 0.009315813019446107, -0.20237558279012427], 'angle_steers': 1.0961167812347412} +{'v_ego': 25.483388900756836, 'd_poly': [-1.2837945469644549e-08, -1.4365089736677789e-05, 0.009583330997975174, -0.1848313071488873], 'angle_steers': 1.1221246719360352} +{'v_ego': 25.47945213317871, 'd_poly': [2.2284031665414895e-08, -1.9874716525699606e-05, 0.009062586145053961, -0.15498677351538215], 'angle_steers': 1.1221227645874023} +{'v_ego': 25.475364685058594, 'd_poly': [1.9216038765266703e-08, -1.8002836204839372e-05, 0.008393301075513351, -0.12553694984451833], 'angle_steers': 1.1313414573669434} +{'v_ego': 25.469839096069336, 'd_poly': [1.4345675937101374e-08, -1.4219613465738837e-05, 0.0072716646163424655, -0.09877442366464534], 'angle_steers': 1.1337530612945557} +{'v_ego': 25.46657943725586, 'd_poly': [1.561155298879375e-08, -8.417546298975094e-06, 0.006051899289626929, -0.08481112299474025], 'angle_steers': 1.2360241413116455} +{'v_ego': 25.45881462097168, 'd_poly': [1.6308521648879145e-08, -2.3145592917840303e-06, 0.004384147654839392, -0.06816017271895998], 'angle_steers': 1.1662907600402832} +{'v_ego': 25.448238372802734, 'd_poly': [8.429462569770378e-08, -1.1147668138266522e-05, 0.004401566767160987, -0.06553284438215065], 'angle_steers': 1.0802662372589111} +{'v_ego': 25.436573028564453, 'd_poly': [1.4937923583348215e-07, -1.8005770680380137e-05, 0.0047423405273560515, -0.07102682980460454], 'angle_steers': 1.0935447216033936} +{'v_ego': 25.434843063354492, 'd_poly': [1.5512624035226132e-07, -1.2992609589350832e-05, 0.003675259030218327, -0.06723329956406227], 'angle_steers': 1.001333475112915} +{'v_ego': 25.442155838012695, 'd_poly': [1.358376729070239e-07, -1.3323161395670424e-05, 0.004286045665281519, -0.07280172066800386], 'angle_steers': 0.9080624580383301} +{'v_ego': 25.426456451416016, 'd_poly': [1.219893791708877e-07, -1.1896978696496202e-05, 0.004078908281067722, -0.07689781083532868], 'angle_steers': 0.6080625057220459} +{'v_ego': 25.420866012573242, 'd_poly': [9.056692394833052e-08, -9.504019934968045e-06, 0.003276171712702275, -0.0731151283040304], 'angle_steers': 0.5159835815429688} +{'v_ego': 25.428817749023438, 'd_poly': [1.4396011687979806e-07, -2.049231362292449e-05, 0.003516203637222829, -0.07404205175125869], 'angle_steers': 0.52205491065979} +{'v_ego': 25.425312042236328, 'd_poly': [1.246001461980902e-07, -2.160713118456901e-05, 0.003654506704922339, -0.07313613611969466], 'angle_steers': 0.5292608737945557} +{'v_ego': 25.46180534362793, 'd_poly': [3.1405869510403796e-07, -4.982656164157515e-05, 0.003904609883999738, -0.07231423684962192], 'angle_steers': 0.3788003921508789} +{'v_ego': 25.486583709716797, 'd_poly': [3.866798183155818e-07, -5.8366557116276855e-05, 0.0038496935698528923, -0.07675087281076466], 'angle_steers': 0.3788115978240967} +{'v_ego': 25.476604461669922, 'd_poly': [3.618706060347675e-07, -6.029709347359867e-05, 0.003928733815794543, -0.08291055447094668], 'angle_steers': 0.2163008451461792} +{'v_ego': 25.4931640625, 'd_poly': [3.961040282681452e-07, -6.395212552491987e-05, 0.003423207750861984, -0.08488351384590409], 'angle_steers': 0.2267833948135376} diff --git a/selfdrive/controls/lib/curvature_learner/cl_data/straight_line_debugging.py b/selfdrive/controls/lib/curvature_learner/cl_data/straight_line_debugging.py new file mode 100644 index 00000000000000..e1a425f86b6a7f --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/cl_data/straight_line_debugging.py @@ -0,0 +1,70 @@ +import math +import os +import json +import time + +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from selfdrive.config import Conversions as CV + +os.chdir(os.getcwd()) + +data = [] +for fi in os.listdir(): + if not fi.endswith('.py') and 'straight' in fi: + print(fi) + with open(fi) as f: + for line in f.read().split('\n')[:-1]: + line = line.replace('array([', '[').replace('])', ']') # formats dpoly's np array + line = line.replace("'", '"') # for json + try: + data.append(json.loads(line)) + except: + raise Exception('error: {}'.format(line)) + +TR = 0.75 +for line in data: + dist = line['v_ego'] * TR + + lat_pos = np.polyval(line['d_poly'], dist) # lateral position in meters at TR seconds + lat_pos -= line['d_poly'][3] # want curvature of road from start of path not car + line['lat_pos'] = lat_pos + +abs_lateral_positions = [abs(line['lat_pos']) for line in data] +abs_angle_steers = [abs(line['angle_steers']) for line in data] +print('max lateral position: {}'.format(max(abs_lateral_positions))) +print('min lateral position: {}'.format(min(abs_lateral_positions))) +print('mean lateral position: {}'.format(np.mean(abs_lateral_positions))) + +plt.figure(0) +# plt.xlim(0, 0.5) +plt.title('lat_pos') +sns.distplot(np.array(abs_lateral_positions)) +plt.show() +# plt.figure(1) +# plt.title('angle_steers') +# sns.distplot(abs_angle_steers) +# plt.show() + + +raise Exception() +plt.figure(2) +for idx, line in enumerate(data[500:]): + plt.clf() + plt.ylim(-1, 1) + # plt.title('sample: {}, speed: {} mph'.format(idx, round(line['v_ego'] * CV.MS_TO_MPH, 2))) + + max_dist = round(line['v_ego'] * TR) + # line['d_poly'][3] = 0 + # print(max_dist) + x = np.linspace(0, max_dist, max_dist) + y = np.polyval(line['d_poly'], x) + plt.plot(x, y, label='original') + plt.plot(x, y - line['d_poly'][3], label='offset by d_poly[3]') + plt.scatter(x[-1], 0, label='zero') + plt.scatter(x[-1], line['lat_pos'], label='curvature_new') + plt.legend() + plt.pause(0.001) + + diff --git a/selfdrive/controls/lib/curvature_learner/cluster_curvature.py b/selfdrive/controls/lib/curvature_learner/cluster_curvature.py new file mode 100644 index 00000000000000..db3a7907f35b52 --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/cluster_curvature.py @@ -0,0 +1,144 @@ +import math +import os +import json +import time + +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from selfdrive.config import Conversions as CV +from sklearn.cluster import KMeans + +os.chdir(os.getcwd() + '/cl_data') +PRINT_MAX_CURV_PER_FILE = False + +data = [] +for fi in os.listdir(): + if not fi.endswith('.py'): + data_this_file = [] + print(fi) + with open(fi) as f: + for line in f.read().split('\n')[:-1]: + line = line.replace('array([', '[').replace('])', ']') # formats dpoly's np array + line = line.replace("'", '"') # for json + try: + data_this_file.append(json.loads(line)) + except: + raise Exception('error: {}'.format(line)) + if PRINT_MAX_CURV_PER_FILE: + print('max curvature: {}'.format(max(np.abs([np.polyval(line['d_poly'], line['v_ego'] * 0.9) - line['d_poly'][3] for line in data_this_file if abs(line['angle_steers']) < 45])))) + data += data_this_file + +print('\nSamples before filtering: {}'.format(len(data))) +ROUND_TO = 8 +MIN_ANGLE = 1 # todo: add a min curvature check once i find a good value for center cluster/band +MAX_ANGLE = (45+90)/2 +TR = 1.2 + +Y_AXIS_KEY = 'true_curvature' +KMEANS_N_CLUSTERS = 8 +KMEANS_MAX_ITER = 2000 +Y_AXIS_WEIGHT = 1.5 # importance of y axis, more clusters for curvature vs. speed + +USE_ABS = True + +data_filtered = [] +for line in data: + if line['v_ego'] < 15 * CV.MPH_TO_MS: + continue + if MIN_ANGLE <= abs(line['angle_steers']) <= MAX_ANGLE: + path_x = np.arange(int(round(line['v_ego'] * TR))) + y_p = 3 * line['d_poly'][0] * path_x ** 2 + 2 * line['d_poly'][1] * path_x + line['d_poly'][2] + y_pp = 6 * line['d_poly'][0] * path_x + 2 * line['d_poly'][1] + curv = y_pp / (1. + y_p ** 2) ** 1.5 + + # curv = np.sqrt(np.clip(np.abs(curv), 1e-8, None)) + curv = np.sqrt(np.abs(curv)) + curv = np.max(curv) # * 7.5 todo: should we multiply? + + # dist = line['v_ego'] * TR + + # lat_pos = np.polyval(line['d_poly'], dist) # lateral position in meters at TR seconds + # lat_pos -= line['d_poly'][3] # want curvature of road from start of path not car + line[Y_AXIS_KEY] = curv + + if USE_ABS: + line[Y_AXIS_KEY] = abs(line[Y_AXIS_KEY]) + data_filtered.append(line) + +print('Samples after filtering: {}'.format(len(data_filtered))) +data = data_filtered + +PLOT_DIST_PLOTS = False +if PLOT_DIST_PLOTS: + plt.figure(0) + sns.distplot([line['v_ego'] for line in data], bins=75) + plt.title('speed (m/s)') + plt.show() + + plt.figure(1) + sns.distplot([line[Y_AXIS_KEY] for line in data], bins=100) + plt.title(Y_AXIS_KEY) + plt.show() + +v_egos = [line['v_ego'] for line in data] +y = np.array([line[Y_AXIS_KEY] for line in data]) +kmeans = KMeans(n_clusters=KMEANS_N_CLUSTERS, max_iter=KMEANS_MAX_ITER) + +y_axis_factor = (max(v_egos) - min(v_egos)) / (max(y) - min(y)) # to make y axis as important as x since it's smaller +y_axis_factor *= Y_AXIS_WEIGHT # x more weight to y axis + +x = np.array([v_egos, y * y_axis_factor]).T +kmeans.fit(x) +cluster_coords = kmeans.cluster_centers_ + + +def find_distance(pt1, pt2): + x1, x2 = pt1[0], pt2[0] + y1, y2 = pt1[1], pt2[1] + return math.hypot(x2 - x1, y2 - y1) + + +t = time.time() +clusters = [[] for _ in range(KMEANS_N_CLUSTERS)] +for line in data: + dists = [find_distance([line['v_ego'], line[Y_AXIS_KEY] * y_axis_factor], clstr_coord) for clstr_coord in cluster_coords] + closest = min(range(len(dists)), key=dists.__getitem__) + clusters[closest].append([line['v_ego'], line[Y_AXIS_KEY]]) +print(time.time() - t) + +plt.figure(3) +for cluster in clusters: + if len(cluster): + d1, d2 = np.array(cluster).T + plt.scatter(d1, d2, s=2) + else: + raise Exception('Cluster cannot be empty') + +plt.xlabel('speed (m/s)') +if Y_AXIS_KEY == 'lat_pos': + plt.ylabel(Y_AXIS_KEY + ' at {} TR'.format(TR)) +else: + plt.ylabel(Y_AXIS_KEY) + +# now print data to copy into curvature_learner.py +print('Number of clusters: {}'.format(KMEANS_N_CLUSTERS)) +cluster_names = [] +final_cluster_list = [] +for idx, cluster_coord in enumerate(sorted(cluster_coords, key=lambda coord: coord[0])): + cluster_name_speed = str(round(cluster_coord[0] * CV.MS_TO_MPH, 1)) + cluster_name_curv = str(round(cluster_coord[1] / y_axis_factor, 2)) + cluster_name = '{}MPH-{}CURV'.format(cluster_name_speed.strip('0'), cluster_name_curv.strip('0')) + + cluster_names.append(cluster_name) + final_cluster_list.append(np.round(cluster_coord, ROUND_TO).tolist()) + print('{}: {}'.format(cluster_name, final_cluster_list[-1])) + plt.scatter(cluster_coord[0], cluster_coord[1] / y_axis_factor, s=100, c='black') + # plt.text(cluster_coord[0] + .38, cluster_coord[1] / y_axis_factor + .03, cluster_name, fontsize=9) + +print('\ncluster_coords = {}'.format(final_cluster_list)) +print('cluster_names = {}'.format(cluster_names)) +print('Make sure to multiply each sample y coordinate by the y axis factor below (y coord of clusters are pre-multiplied)!') +print('y axis factor: {}'.format(round(y_axis_factor, ROUND_TO))) + +plt.show() diff --git a/selfdrive/controls/lib/curvature_learner/cluster_lat_pos.py b/selfdrive/controls/lib/curvature_learner/cluster_lat_pos.py new file mode 100644 index 00000000000000..ce5b24bf4afcb6 --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/cluster_lat_pos.py @@ -0,0 +1,135 @@ +import math +import os +import json +import time + +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from selfdrive.config import Conversions as CV +from sklearn.cluster import KMeans + +os.chdir(os.getcwd() + '/cl_data') +PRINT_MAX_CURV_PER_FILE = False + +data = [] +for fi in os.listdir(): + if not fi.endswith('.py'): + data_this_file = [] + print(fi) + with open(fi) as f: + for line in f.read().split('\n')[:-1]: + line = line.replace('array([', '[').replace('])', ']') # formats dpoly's np array + line = line.replace("'", '"') # for json + try: + data_this_file.append(json.loads(line)) + except: + raise Exception('error: {}'.format(line)) + if PRINT_MAX_CURV_PER_FILE: + print('max curvature: {}'.format(max(np.abs([np.polyval(line['d_poly'], line['v_ego'] * 0.9) - line['d_poly'][3] for line in data_this_file if abs(line['angle_steers']) < 45])))) + data += data_this_file + +print('\nSamples before filtering: {}'.format(len(data))) +ROUND_TO = 8 +MIN_ANGLE = 1 # todo: add a min curvature check once i find a good value for center cluster/band +MAX_ANGLE = 45. +TR = 0.75 + +Y_AXIS_KEY = 'lat_pos' +KMEANS_N_CLUSTERS = 12 +KMEANS_MAX_ITER = 2000 +Y_AXIS_WEIGHT = 1.05 # importance of y axis, more clusters for curvature vs. speed + +USE_ABS = True + +data_filtered = [] +for line in data: + if line['v_ego'] < 15 * CV.MPH_TO_MS: + continue + if MIN_ANGLE <= abs(line['angle_steers']) <= MAX_ANGLE: + dist = line['v_ego'] * TR + + lat_pos = np.polyval(line['d_poly'], dist) # lateral position in meters at TR seconds + lat_pos -= line['d_poly'][3] # want curvature of road from start of path not car + line['lat_pos'] = lat_pos + + if USE_ABS: + line['lat_pos'] = abs(line['lat_pos']) + data_filtered.append(line) + +print('Samples after filtering: {}'.format(len(data_filtered))) +data = data_filtered + +PLOT_DIST_PLOTS = True +if PLOT_DIST_PLOTS: + plt.figure(0) + sns.distplot([line['v_ego'] for line in data], bins=75) + plt.title('speed (m/s)') + plt.show() + + plt.figure(1) + sns.distplot([line[Y_AXIS_KEY] for line in data], bins=100) + plt.title(Y_AXIS_KEY) + plt.show() + +v_egos = [line['v_ego'] for line in data] +y = np.array([line[Y_AXIS_KEY] for line in data]) +kmeans = KMeans(n_clusters=KMEANS_N_CLUSTERS, max_iter=KMEANS_MAX_ITER) + +y_axis_factor = (max(v_egos) - min(v_egos)) / (max(y) - min(y)) # to make y axis as important as x since it's smaller +y_axis_factor *= Y_AXIS_WEIGHT # x more weight to y axis + +x = np.array([v_egos, y * y_axis_factor]).T +kmeans.fit(x) +cluster_coords = kmeans.cluster_centers_ + + +def find_distance(pt1, pt2): + x1, x2 = pt1[0], pt2[0] + y1, y2 = pt1[1], pt2[1] + return math.hypot(x2 - x1, y2 - y1) + + +t = time.time() +clusters = [[] for _ in range(KMEANS_N_CLUSTERS)] +for line in data: + dists = [find_distance([line['v_ego'], line[Y_AXIS_KEY] * y_axis_factor], clstr_coord) for clstr_coord in cluster_coords] + closest = min(range(len(dists)), key=dists.__getitem__) + clusters[closest].append([line['v_ego'], line[Y_AXIS_KEY]]) +print(time.time() - t) + +plt.figure(3) +for cluster in clusters: + if len(cluster): + d1, d2 = np.array(cluster).T + plt.scatter(d1, d2, s=2) + else: + raise Exception('Cluster cannot be empty') + +plt.xlabel('speed (m/s)') +if Y_AXIS_KEY == 'lat_pos': + plt.ylabel(Y_AXIS_KEY + ' at {} TR'.format(TR)) +else: + plt.ylabel(Y_AXIS_KEY) + +# now print data to copy into curvature_learner.py +print('Number of clusters: {}'.format(KMEANS_N_CLUSTERS)) +cluster_names = [] +final_cluster_list = [] +for idx, cluster_coord in enumerate(sorted(cluster_coords, key=lambda coord: coord[0])): + cluster_name_speed = str(round(cluster_coord[0] * CV.MS_TO_MPH, 1)) + cluster_name_curv = str(round(cluster_coord[1] / y_axis_factor, 2)) + cluster_name = '{}MPH-{}CURV'.format(cluster_name_speed.strip('0'), cluster_name_curv.strip('0')) + + cluster_names.append(cluster_name) + final_cluster_list.append(np.round(cluster_coord, ROUND_TO).tolist()) + print('{}: {}'.format(cluster_name, final_cluster_list[-1])) + plt.scatter(cluster_coord[0], cluster_coord[1] / y_axis_factor, s=100, c='black') + # plt.text(cluster_coord[0] + .38, cluster_coord[1] / y_axis_factor + .03, cluster_name, fontsize=9) + +print('\ncluster_coords = {}'.format(final_cluster_list)) +print('cluster_names = {}'.format(cluster_names)) +print('Make sure to multiply each sample y coordinate by the y axis factor below (y coord of clusters are pre-multiplied)!') +print('y axis factor: {}'.format(round(y_axis_factor, ROUND_TO))) + +plt.show() diff --git a/selfdrive/controls/lib/curvature_learner/compare_zorrobytes_learner.py b/selfdrive/controls/lib/curvature_learner/compare_zorrobytes_learner.py new file mode 100644 index 00000000000000..78098f216f364e --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/compare_zorrobytes_learner.py @@ -0,0 +1,37 @@ +learned_offset = 0 +learning_rate = 50 + +CURVE_DIRECTION = 'left' +if CURVE_DIRECTION == 'left': + lat_pos = 0.5 + angle_steers = 5 +else: + lat_pos = -0.5 + angle_steers = -5 + +direction = 'left' if lat_pos > 0 else 'right' + +HUGGING_DIRECTION = 'left' +if HUGGING_DIRECTION == 'left': + d_poly = [0, 0, 0, -0.5] +else: + d_poly = [0, 0, 0, 0.5] + +if angle_steers > 0.1: + print('wheel angled left') + learned_offset -= d_poly[3] / learning_rate + offset = learned_offset + +elif angle_steers < -0.1: + print('wheel angled right') + learned_offset += d_poly[3] / learning_rate + offset = learned_offset + +print('zorrobyte\'s learned_offset: {}'.format(learned_offset)) + +if direction == 'right': + d_poly[3] = -d_poly[3] # d_poly's sign switches for oversteering in different directions +learned_offset = 0 +learned_offset -= d_poly[3] / learning_rate + +print('new learned_offset: {}'.format(learned_offset)) diff --git a/selfdrive/controls/lib/curvature_learner/curvature_learner_latest_not_working.py b/selfdrive/controls/lib/curvature_learner/curvature_learner_latest_not_working.py new file mode 100644 index 00000000000000..7717c4dff936e2 --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/curvature_learner_latest_not_working.py @@ -0,0 +1,134 @@ +import os +import math +import json +from common.numpy_fast import clip +from common.realtime import sec_since_boot +from selfdrive.config import Conversions as CV +from selfdrive.controls.lib.lane_planner import eval_poly +from common.op_params import opParams + +# CurvatureLearner v4 by Zorrobyte +# Modified to add direction as a learning factor as well as clusters based on speed x curvature (lateral pos in 0.9 seconds) +# Clusters found with sklearn KMeans, this assigns more clusters to areas with more data and viceversa +# Version 5 due to json incompatibilities + +GATHER_DATA = True +VERSION = 5.6 + +FT_TO_M = 0.3048 + + +def find_distance(pt1, pt2): + x1, x2 = pt1[0], pt2[0] + y1, y2 = pt1[1], pt2[1] + return math.hypot(x2 - x1, y2 - y1) + + +class CurvatureLearner: + def __init__(self): + self.op_params = opParams() + self.curvature_file = '/data/curvature_offsets.json' + self.fast_learn_file = '/data/curvature_fast_learn.json' + rate = 1 / 20. # pathplanner is 20 hz + self.learning_rate = 2.5833e-3 * rate + self.write_frequency = 5 # in seconds + self.min_lr_prob = .65 + self.min_speed = 15 * CV.MPH_TO_MS + + self.y_axis_factor = 14.52823993 # weight y/curvature as much as speed + self.min_curvature = 0.059954 # ~0.8 degrees + + self.directions = ['left', 'right'] + self.cluster_coords = [[10.02332922, 1.2082594], [11.99819907, 13.79344136], [12.02127065, 5.86476815], [14.61702255, 1.46308569], [18.4381052, 5.74835111], [18.61637118, 1.56647061], + [22.04819156, 10.67969111], [22.79735722, 1.8271313], [23.63347784, 6.11923985], [23.71383645, 16.63300599], [26.80928261, 10.30835547], [26.86438932, 1.95377927], + [29.45564774, 4.92064132]] + # self.cluster_names = ['CLUSTER_{}'.format(idx) for idx in range(len(self.cluster_coords))] + self.cluster_names = ['22.4MPH-.08CURV', '26.8MPH-.95CURV', '26.9MPH-.4CURV', '32.7MPH-.1CURV', '41.2MPH-.4CURV', '41.6MPH-.11CURV', '49.3MPH-.74CURV', '51.MPH-.13CURV', '52.9MPH-.42CURV', + '53.MPH-1.14CURV', '60.MPH-.71CURV', '60.1MPH-.13CURV', '65.9MPH-.34CURV'] + + self.fast_learning_for = 90 # seconds per cluster + self.fast_learning_for = round(self.fast_learning_for / rate) # speed up learning first time user uses curvature learner + self.fast_lr_multiplier = 2. # 2x faster learning until ~1 MIN for each cluster + self._load_curvature() + + def update(self, v_ego, d_poly, lane_probs, angle_steers): + self._gather_data(v_ego, d_poly, angle_steers) + offset = 0 + if v_ego < self.min_speed or math.isnan(d_poly[0]) or len(d_poly) != 4 or not self.op_params.get('curvature_learner'): + return offset + + cluster, direction = self.cluster_sample(v_ego, d_poly) + if cluster is not None: # don't learn/return an offset if below min curvature + lr_prob = lane_probs[0] + lane_probs[1] - lane_probs[0] * lane_probs[1] + + if lr_prob >= self.min_lr_prob: # only learn when lane lines are present; still use existing offset + if direction == 'right': + d_poly[3] = -d_poly[3] # d_poly's sign switches for oversteering in different directions + + lr = self.get_learning_rate(direction, cluster) # faster learning for first ~minute per cluster + self.learned_offsets[direction][cluster] -= d_poly[3] * lr # the learning + offset = self.learned_offsets[direction][cluster] + + self._write_data() + return clip(offset, -0.3, 0.3) + + def cluster_sample(self, v_ego, d_poly): + TR = 0.9 + dist = v_ego * TR + # we want curvature of road from start of path not car, so subtract d_poly[3] + lat_pos = eval_poly(d_poly, dist) - d_poly[3] # lateral position in meters at TR seconds + direction = 'left' if lat_pos > 0 else 'right' + + lat_pos = abs(lat_pos) + closest_cluster = None + if lat_pos >= self.min_curvature: + sample_coord = [v_ego, lat_pos * self.y_axis_factor] # we multiply y so that the dist function weights x and y the same + dists = [find_distance(sample_coord, cluster_coord) for cluster_coord in self.cluster_coords] # todo: remove clusters far away based on v_ego to speed this up + closest_cluster = self.cluster_names[min(range(len(dists)), key=dists.__getitem__)] + + return closest_cluster, direction + + def get_learning_rate(self, direction, cluster): + lr = self.learning_rate + fast_iter_left = self.fast_learn[direction][cluster] + if not isinstance(fast_iter_left, str): + if 1 <= fast_iter_left: # decrement until we reach 0 + self.fast_learn[direction][cluster] -= 1 + lr *= self.fast_lr_multiplier + else: # mark done + self.fast_learn[direction][cluster] = 'done' + return lr + + def _gather_data(self, v_ego, d_poly, angle_steers): + if GATHER_DATA: + with open('/data/curv_learner_data', 'a') as f: + f.write('{}\n'.format({'v_ego': v_ego, 'd_poly': list(d_poly), 'angle_steers': angle_steers})) + + def _load_curvature(self): + self._last_write_time = 0 + try: + with open(self.curvature_file, 'r') as f: + self.learned_offsets = json.load(f) + with open(self.fast_learn_file, 'r') as f: + self.fast_learn = json.load(f) + if 'version' in self.learned_offsets and self.learned_offsets['version'] == VERSION: + return + except: + pass + # can't read file, doesn't exist, or old version + # todo: old: self.learned_offsets = {d: {c: {'offset': 0., 'fast_learn': self.fast_learning_for} for c in self.cluster_names} for d in self.directions} + self.learned_offsets = {d: {c: 0. for c in self.cluster_names} for d in self.directions} + self.fast_learn = {d: {c: self.fast_learning_for for c in self.cluster_names} for d in self.directions} + self.learned_offsets['version'] = VERSION # update version + self._write_data() # rewrite/create new file + + def _write_data(self): + if sec_since_boot() - self._last_write_time >= self.write_frequency: + with open(self.curvature_file, 'w') as f: + f.write(json.dumps(self.learned_offsets, indent=2)) + with open(self.fast_learn_file, 'w') as f: + f.write(json.dumps(self.fast_learn, indent=2)) + + os.chmod(self.curvature_file, 0o777) + os.chmod(self.fast_learn_file, 0o777) + self._last_write_time = sec_since_boot() diff --git a/selfdrive/controls/lib/curvature_learner/map-angle-to-curvature.py b/selfdrive/controls/lib/curvature_learner/map-angle-to-curvature.py new file mode 100644 index 00000000000000..e4088d3b600e7c --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/map-angle-to-curvature.py @@ -0,0 +1,136 @@ +import os +import json +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from selfdrive.config import Conversions as CV + + +os.chdir(os.getcwd() + '/cl_data') + +data = [] +for fi in os.listdir(): + print(fi) + if not fi.endswith('.py'): + with open(fi) as f: + for line in f.read().split('\n')[:-1]: + line = line.replace('array([', '[').replace('])', ']') # formats dpoly's np array + line = line.replace("'", '"') # for json + try: + data.append(json.loads(line)) + except: + print('error: {}'.format(line)) + +round_to = 6 +min_angle = 1. +center_max = 2.5 +inner_max = 7.5 +outer_max = 15. +sharp_max = float('inf') +data_banded = {'center': [], 'inner': [], 'outer': [], 'sharp': []} +min_angles = [] + +within_percent = 0.075 # only use angles within x% lower than max angle to get higher max average (instead of max()) + +for line in data: + if line['v_ego'] < 15 * CV.MPH_TO_MS: + continue + angle_steers = line['angle_steers'] + + if abs(angle_steers) < min_angle * (1 + within_percent): + if min_angle * (1 - within_percent) < abs(angle_steers): + min_angles.append(line) + + if abs(angle_steers) >= min_angle: + if abs(angle_steers) < center_max * (1 + within_percent): + if center_max * (1 - within_percent) < abs(angle_steers): # within x% + data_banded['center'].append(line) + continue + if abs(angle_steers) < inner_max * (1 + within_percent): + if inner_max * (1 - within_percent) < abs(angle_steers): + data_banded['inner'].append(line) + continue + if abs(angle_steers) < outer_max * (1 + within_percent): + if outer_max * (1 - within_percent) < abs(angle_steers): + data_banded['outer'].append(line) + continue + data_banded['sharp'].append(line) + +for band in data_banded: + print('{}: {}'.format(band, len(data_banded[band])), end=' ') +print('min_angles: {}'.format(len(min_angles))) +print() + +avg_angle_bands = {} +max_angle_text = {'center': '{} deg'.format(center_max), + 'inner': '{} deg'.format(inner_max), + 'outer': '{} deg'.format(outer_max), + 'sharp': 'doen\'t matter as much/inf'} +for band in data_banded: + avg_angle_bands[band] = np.mean([abs(line['angle_steers']) for line in data_banded[band]]) + print('MA {} angle: {} ({})'.format(band, round(avg_angle_bands[band], round_to), max_angle_text[band])) +print() + +TR = 1.2 +stds = {} +min_curvatures = [] +curvature_dict = {'center': [], 'inner': [], 'outer': [], 'sharp': []} +for band in data_banded: + for line in data_banded[band]: + path_x = np.arange(int(round(line['v_ego'] * TR))) # evaluate out to 0.9 seconds + y_p = 3 * line['d_poly'][0] * path_x ** 2 + 2 * line['d_poly'][1] * path_x + line['d_poly'][2] + y_pp = 6 * line['d_poly'][0] * path_x + 2 * line['d_poly'][1] + curv = y_pp / (1. + y_p ** 2) ** 1.5 + + # curv = np.sqrt(np.clip(np.abs(curv), 1e-8, None)) + curv = np.sqrt(np.abs(curv)) + curv = np.max(curv) + + curvature_dict[band].append(abs(curv)) + +for line in min_angles: + # dist = line['v_ego'] * TR + # line['d_poly'][3] = 0 # want curvature of road from start of path not car + # lat_pos = np.polyval(line['d_poly'], dist) # lateral position in meters at TR seconds + path_x = np.arange(int(round(line['v_ego'] * TR))) # evaluate out to 0.9 seconds + y_p = 3 * line['d_poly'][0] * path_x ** 2 + 2 * line['d_poly'][1] * path_x + line['d_poly'][2] + y_pp = 6 * line['d_poly'][0] * path_x + 2 * line['d_poly'][1] + curv = y_pp / (1. + y_p ** 2) ** 1.5 + + # curv = np.sqrt(np.clip(np.abs(curv), 1e-8, None)) + curv = np.sqrt(np.abs(curv)) + curv = np.max(curv) + min_curvatures.append(abs(curv)) + +avg_curvatures = {band: np.mean(curvature_dict[band]) for band in curvature_dict} +std_curvatures = {band: np.std(curvature_dict[band]) for band in curvature_dict} + +modifiers = {} +modifiers['center'] = center_max / avg_angle_bands['center'] # just since average angle doesn't equal the exact max limit +modifiers['inner'] = inner_max / avg_angle_bands['inner'] +modifiers['outer'] = outer_max / avg_angle_bands['outer'] +modifiers['sharp'] = 1 # we don't even use this +stds['1.8'] = np.mean([std_curvatures['inner'], std_curvatures['outer']]) + +for band in avg_curvatures: + print('MA {} curvature: {}, std: {} (ADJUSTED: {})'.format(band, round(avg_curvatures[band], round_to), + round(std_curvatures[band], round_to), + round(avg_curvatures[band] * modifiers[band], round_to))) + +min_curv_from_angle = (min_angle * avg_curvatures['center']) / avg_angle_bands['center'] # calcs 0.1 deg min equivelent +print('min calc. curvature for CL: {} (ADJUSTED: {})'.format(round(min_curv_from_angle, round_to), round(min_curv_from_angle * modifiers['center'], round_to))) +print('min curvature from data: {}'.format(round(np.mean(min_curvatures), round_to))) + +# print() +# stds_sorted = sorted(stds, reverse=False, key=stds.get) +# for std in stds_sorted: +# print('TR: {}, std: {}'.format(std, stds[std])) + +for idx, band in enumerate(curvature_dict): + plt.figure(idx) + sns.distplot(curvature_dict[band]) + plt.title(band) +plt.figure(len(curvature_dict)) +sns.distplot(min_curvatures, bins=500) +plt.title('min_curvatures') +plt.show() diff --git a/selfdrive/controls/lib/curvature_learner/map-angle-to-lat-pos.py b/selfdrive/controls/lib/curvature_learner/map-angle-to-lat-pos.py new file mode 100644 index 00000000000000..c34195af7612f6 --- /dev/null +++ b/selfdrive/controls/lib/curvature_learner/map-angle-to-lat-pos.py @@ -0,0 +1,126 @@ +import os +import json +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from selfdrive.config import Conversions as CV + + +os.chdir(os.getcwd() + '/cl_data') + +data = [] +for fi in os.listdir(): + print(fi) + if not fi.endswith('.py'): + with open(fi) as f: + for line in f.read().split('\n')[:-1]: + line = line.replace('array([', '[').replace('])', ']') # formats dpoly's np array + line = line.replace("'", '"') # for json + try: + data.append(json.loads(line)) + except: + print('error: {}'.format(line)) + +round_to = 6 +min_angle = 1. +center_max = 2.5 +inner_max = 7.5 +outer_max = 15. +sharp_max = float('inf') +data_banded = {'center': [], 'inner': [], 'outer': [], 'sharp': []} +min_angles = [] + +within_percent = 0.075 # only use angles within x% lower than max angle to get higher max average (instead of max()) + +for line in data: + if line['v_ego'] < 15 * CV.MPH_TO_MS: + continue + angle_steers = line['angle_steers'] + + if abs(angle_steers) < min_angle * (1 + within_percent): + if min_angle * (1 - within_percent) < abs(angle_steers): + min_angles.append(line) + + if abs(angle_steers) >= min_angle: + if abs(angle_steers) < center_max * (1 + within_percent): + if center_max * (1 - within_percent) < abs(angle_steers): # within x% + data_banded['center'].append(line) + continue + if abs(angle_steers) < inner_max * (1 + within_percent): + if inner_max * (1 - within_percent) < abs(angle_steers): + data_banded['inner'].append(line) + continue + if abs(angle_steers) < outer_max * (1 + within_percent): + if outer_max * (1 - within_percent) < abs(angle_steers): + data_banded['outer'].append(line) + continue + data_banded['sharp'].append(line) + +for band in data_banded: + print('{}: {}'.format(band, len(data_banded[band])), end=' ') +print('min_angles: {}'.format(len(min_angles))) +print() + +avg_angle_bands = {} +max_angle_text = {'center': '{} deg'.format(center_max), + 'inner': '{} deg'.format(inner_max), + 'outer': '{} deg'.format(outer_max), + 'sharp': 'doen\'t matter as much/inf'} +for band in data_banded: + avg_angle_bands[band] = np.mean([abs(line['angle_steers']) for line in data_banded[band]]) + print('MA {} angle: {} ({})'.format(band, round(avg_angle_bands[band], round_to), max_angle_text[band])) +print() + +# TR = 1.8 +TRs = np.linspace(0.4, 2.7, 24*2) +TRs = [0.9] +stds = {} +min_curvatures = [] +for TR in TRs: + curvature_dict = {'center': [], 'inner': [], 'outer': [], 'sharp': []} + for band in data_banded: + for line in data_banded[band]: + dist = line['v_ego'] * TR + line['d_poly'][3] = 0 # want curvature of road from start of path not car + lat_pos = np.polyval(line['d_poly'], dist) # lateral position in meters at TR seconds + curvature_dict[band].append(abs(lat_pos)) + + for line in min_angles: + dist = line['v_ego'] * TR + line['d_poly'][3] = 0 # want curvature of road from start of path not car + lat_pos = np.polyval(line['d_poly'], dist) # lateral position in meters at TR seconds + min_curvatures.append(abs(lat_pos)) + + avg_curvatures = {band: np.mean(curvature_dict[band]) for band in curvature_dict} + std_curvatures = {band: np.std(curvature_dict[band]) for band in curvature_dict} + + modifiers = {} + modifiers['center'] = center_max / avg_angle_bands['center'] # just since average angle doesn't equal the exact max limit + modifiers['inner'] = inner_max / avg_angle_bands['inner'] + modifiers['outer'] = outer_max / avg_angle_bands['outer'] + modifiers['sharp'] = 1 # we don't even use this + stds[TR] = np.mean([std_curvatures['inner'], std_curvatures['outer']]) + + for band in avg_curvatures: + print('MA {} curvature: {}, std: {} (ADJUSTED: {})'.format(band, round(avg_curvatures[band], round_to), + round(std_curvatures[band], round_to), + round(avg_curvatures[band] * modifiers[band], round_to))) + + min_curv_from_angle = (min_angle * avg_curvatures['center']) / avg_angle_bands['center'] # calcs 0.1 deg min equivelent + print('min calc. curvature for CL: {} (ADJUSTED: {})'.format(round(min_curv_from_angle, round_to), round(min_curv_from_angle * modifiers['center'], round_to))) + print('min curvature from data: {}'.format(round(np.mean(min_curvatures), round_to))) + print('TR: {}'.format(TR)) + +# print() +# stds_sorted = sorted(stds, reverse=False, key=stds.get) +# for std in stds_sorted: +# print('TR: {}, std: {}'.format(std, stds[std])) + +for idx, band in enumerate(curvature_dict): + plt.figure(idx) + sns.distplot(curvature_dict[band]) + plt.title(band) +plt.figure(len(curvature_dict)) +sns.distplot(min_curvatures, bins=500) +plt.title('min_curvatures') +plt.show() diff --git a/selfdrive/controls/lib/pathplanner.py b/selfdrive/controls/lib/pathplanner.py index 35edcc837fd360..60c1fc2eec29d2 100644 --- a/selfdrive/controls/lib/pathplanner.py +++ b/selfdrive/controls/lib/pathplanner.py @@ -5,6 +5,7 @@ from selfdrive.controls.lib.lateral_mpc import libmpc_py from selfdrive.controls.lib.drive_helpers import MPC_COST_LAT from selfdrive.controls.lib.lane_planner import LanePlanner +from selfdrive.controls.lib.curvature_learner import CurvatureLearner from selfdrive.config import Conversions as CV from common.params import Params import cereal.messaging as messaging @@ -66,6 +67,7 @@ def __init__(self, CP): self.op_params = opParams() self.alca_nudge_required = self.op_params.get('alca_nudge_required') self.alca_min_speed = self.op_params.get('alca_min_speed') * CV.MPH_TO_MS + self.curvature_learner = CurvatureLearner() def setup_mpc(self): self.libmpc = libmpc_py.libmpc @@ -98,7 +100,7 @@ def update(self, sm, pm, CP, VM): sr = max(sm['liveParameters'].steerRatio, 0.1) VM.update_params(x, sr) - curvature_factor = VM.curvature_factor(v_ego) + curvature_factor = float(VM.curvature_factor(v_ego)) self.LP.parse_model(sm['model']) @@ -171,6 +173,12 @@ def update(self, sm, pm, CP, VM): self.LP.r_prob *= self.lane_change_ll_prob self.LP.update_d_poly(v_ego, angle_steers, active) + if active: + # print('old: {}'.format(curvature_factor)) + curvature_factor -= self.curvature_learner.update(v_ego, self.LP.d_poly, [self.LP.l_prob, self.LP.r_prob], angle_steers - angle_offset) + # print('new: {}'.format(curvature_factor)) + # print('---') + # account for actuation delay self.cur_state = calc_states_after_delay(self.cur_state, v_ego, angle_steers - angle_offset, curvature_factor, VM.sR, CP.steerActuatorDelay) diff --git a/selfdrive/manager.py b/selfdrive/manager.py index 0a80f51d022272..176c069219c5b9 100755 --- a/selfdrive/manager.py +++ b/selfdrive/manager.py @@ -494,7 +494,7 @@ def manager_thread(): # check the status of all processes, did any of them die? running_list = ["%s%s\u001b[0m" % ("\u001b[32m" if running[p].is_alive() else "\u001b[31m", p) for p in running] - cloudlog.debug(' '.join(running_list)) + # cloudlog.debug(' '.join(running_list)) # Exit main loop when uninstall is needed if params.get("DoUninstall", encoding='utf8') == "1": From 3412a49c9c45d775345b617066b34b941755383d Mon Sep 17 00:00:00 2001 From: Shane Smiskol Date: Mon, 14 Sep 2020 14:31:49 -0500 Subject: [PATCH 2/2] add CL param --- common/op_params.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/common/op_params.py b/common/op_params.py index b020a9c76c1da9..7dd32d1d9559bb 100644 --- a/common/op_params.py +++ b/common/op_params.py @@ -92,7 +92,8 @@ def __init__(self): 'localizer might not work correctly'), 'prius_use_pid': Param(False, bool, 'This enables the PID lateral controller with new a experimental derivative tune\nFalse: stock INDI, True: TSS2-tuned PID'), 'corolla_use_lqr': Param(False, bool, 'Enable this to use LQR for lateral control with your TSS1 Corolla\nFalse: PID, True: RAV4-tuned LQR'), - 'corollaTSS2_use_indi': Param(False, bool, 'Enable this to use INDI for lat with your Corolla with TSS2')} + 'corollaTSS2_use_indi': Param(False, bool, 'Enable this to use INDI for lat with your Corolla with TSS2'), + 'curvature_learner': Param(True, bool, 'Curvature Learner v5 includes a few new factors that it learns with, keep enabled if your car hugs in certain curves', live=True),} self._params_file = '/data/op_params.json' self._backup_file = '/data/op_params_corrupt.json'