Vehicle Researcher
6 years ago
parent
573a6915fc
commit
210db686bb
36 changed files with 825 additions and 159 deletions
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libopenblas_armv8p-r0.2.19.so |
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#define COMMA_VERSION "0.5.6-release" |
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#define COMMA_VERSION "0.5.7-release" |
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#!/usr/bin/env python |
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# Add phonelibs openblas to LD_LIBRARY_PATH if import fails |
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try: |
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from scipy import spatial |
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except ImportError as e: |
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import os |
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import sys |
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from common.basedir import BASEDIR |
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openblas_path = os.path.join(BASEDIR, "phonelibs/openblas/") |
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os.environ['LD_LIBRARY_PATH'] += ':' + openblas_path |
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args = [sys.executable] |
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args.extend(sys.argv) |
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os.execv(sys.executable, args) |
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import time |
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import zmq |
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import threading |
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import numpy as np |
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import overpy |
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from collections import defaultdict |
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from common.transformations.coordinates import geodetic2ecef |
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from selfdrive.services import service_list |
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import selfdrive.messaging as messaging |
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from mapd_helpers import LOOKAHEAD_TIME, MAPS_LOOKAHEAD_DISTANCE, Way, circle_through_points |
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OVERPASS_API_URL = "https://overpass.kumi.systems/api/interpreter" |
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OVERPASS_HEADERS = { |
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'User-Agent': 'NEOS (comma.ai)' |
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} |
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last_gps = None |
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query_lock = threading.Lock() |
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last_query_result = None |
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last_query_pos = None |
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def build_way_query(lat, lon, radius=50): |
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"""Builds a query to find all highways within a given radius around a point""" |
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pos = " (around:%f,%f,%f)" % (radius, lat, lon) |
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q = """( |
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way |
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""" + pos + """ |
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[highway]; |
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>;);out; |
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""" |
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return q |
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def query_thread(): |
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global last_query_result, last_query_pos |
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api = overpy.Overpass(url=OVERPASS_API_URL, headers=OVERPASS_HEADERS, timeout=10.) |
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while True: |
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if last_gps is not None: |
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fix_ok = last_gps.flags & 1 |
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if not fix_ok: |
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continue |
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if last_query_pos is not None: |
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cur_ecef = geodetic2ecef((last_gps.latitude, last_gps.longitude, last_gps.altitude)) |
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prev_ecef = geodetic2ecef((last_query_pos.latitude, last_query_pos.longitude, last_query_pos.altitude)) |
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dist = np.linalg.norm(cur_ecef - prev_ecef) |
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if dist < 1000: |
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continue |
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q = build_way_query(last_gps.latitude, last_gps.longitude, radius=2000) |
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try: |
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new_result = api.query(q) |
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# Build kd-tree |
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nodes = [] |
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real_nodes = [] |
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node_to_way = defaultdict(list) |
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for n in new_result.nodes: |
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nodes.append((float(n.lat), float(n.lon), 0)) |
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real_nodes.append(n) |
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for way in new_result.ways: |
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for n in way.nodes: |
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node_to_way[n.id].append(way) |
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nodes = np.asarray(nodes) |
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nodes = geodetic2ecef(nodes) |
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tree = spatial.cKDTree(nodes) |
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query_lock.acquire() |
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last_query_result = new_result, tree, real_nodes, node_to_way |
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last_query_pos = last_gps |
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query_lock.release() |
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except Exception as e: |
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print e |
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query_lock.acquire() |
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last_query_result = None |
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query_lock.release() |
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time.sleep(1) |
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def mapsd_thread(): |
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global last_gps |
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context = zmq.Context() |
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gps_sock = messaging.sub_sock(context, service_list['gpsLocation'].port, conflate=True) |
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gps_external_sock = messaging.sub_sock(context, service_list['gpsLocationExternal'].port, conflate=True) |
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map_data_sock = messaging.pub_sock(context, service_list['liveMapData'].port) |
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cur_way = None |
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curvature_valid = False |
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curvature = None |
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upcoming_curvature = 0. |
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dist_to_turn = 0. |
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road_points = None |
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xx = np.arange(0, MAPS_LOOKAHEAD_DISTANCE, 10) |
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while True: |
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gps = messaging.recv_one(gps_sock) |
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gps_ext = messaging.recv_one_or_none(gps_external_sock) |
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if gps_ext is not None: |
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gps = gps_ext.gpsLocationExternal |
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else: |
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gps = gps.gpsLocation |
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last_gps = gps |
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fix_ok = gps.flags & 1 |
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if not fix_ok or last_query_result is None: |
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cur_way = None |
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curvature = None |
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curvature_valid = False |
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upcoming_curvature = 0. |
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dist_to_turn = 0. |
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road_points = None |
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else: |
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lat = gps.latitude |
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lon = gps.longitude |
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heading = gps.bearing |
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speed = gps.speed |
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query_lock.acquire() |
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cur_way = Way.closest(last_query_result, lat, lon, heading) |
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if cur_way is not None: |
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pnts, curvature_valid = cur_way.get_lookahead(last_query_result, lat, lon, heading, MAPS_LOOKAHEAD_DISTANCE) |
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xs = pnts[:, 0] |
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ys = pnts[:, 1] |
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road_points = map(float, xs), map(float, ys) |
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if speed < 10: |
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curvature_valid = False |
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# The curvature is valid when at least MAPS_LOOKAHEAD_DISTANCE of road is found |
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if curvature_valid: |
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# Compute the curvature for each point |
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with np.errstate(divide='ignore'): |
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circles = [circle_through_points(*p) for p in zip(pnts, pnts[1:], pnts[2:])] |
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circles = np.asarray(circles) |
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radii = np.nan_to_num(circles[:, 2]) |
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radii[radii < 10] = np.inf |
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curvature = 1. / radii |
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# Index of closest point |
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closest = np.argmin(np.linalg.norm(pnts, axis=1)) |
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dist_to_closest = pnts[closest, 0] # We can use x distance here since it should be close |
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# Compute distance along path |
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dists = list() |
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dists.append(0) |
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for p, p_prev in zip(pnts, pnts[1:, :]): |
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dists.append(dists[-1] + np.linalg.norm(p - p_prev)) |
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dists = np.asarray(dists) |
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dists = dists - dists[closest] + dist_to_closest |
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# TODO: Determine left or right turn |
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curvature = np.nan_to_num(curvature) |
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curvature_interp = np.interp(xx, dists[1:-1], curvature) |
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curvature_lookahead = curvature_interp[:int(speed * LOOKAHEAD_TIME / 10)] |
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# Outlier rejection |
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new_curvature = np.percentile(curvature_lookahead, 90) |
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k = 0.9 |
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upcoming_curvature = k * upcoming_curvature + (1 - k) * new_curvature |
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in_turn_indices = curvature_interp > 0.8 * new_curvature |
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if np.any(in_turn_indices): |
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dist_to_turn = np.min(xx[in_turn_indices]) |
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else: |
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dist_to_turn = 999 |
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query_lock.release() |
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dat = messaging.new_message() |
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dat.init('liveMapData') |
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if last_gps is not None: |
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dat.liveMapData.lastGps = last_gps |
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if cur_way is not None: |
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dat.liveMapData.wayId = cur_way.id |
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# Seed limit |
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max_speed = cur_way.max_speed |
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if max_speed is not None: |
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dat.liveMapData.speedLimitValid = True |
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dat.liveMapData.speedLimit = max_speed |
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# Curvature |
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dat.liveMapData.curvatureValid = curvature_valid |
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dat.liveMapData.curvature = float(upcoming_curvature) |
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dat.liveMapData.distToTurn = float(dist_to_turn) |
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if road_points is not None: |
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dat.liveMapData.roadX, dat.liveMapData.roadY = road_points |
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if curvature is not None: |
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dat.liveMapData.roadCurvatureX = map(float, xx) |
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dat.liveMapData.roadCurvature = map(float, curvature_interp) |
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map_data_sock.send(dat.to_bytes()) |
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def main(gctx=None): |
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main_thread = threading.Thread(target=mapsd_thread) |
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main_thread.daemon = True |
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main_thread.start() |
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q_thread = threading.Thread(target=query_thread) |
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q_thread.daemon = True |
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q_thread.start() |
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while True: |
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time.sleep(0.1) |
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if __name__ == "__main__": |
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main() |
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import math |
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import numpy as np |
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from datetime import datetime |
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from selfdrive.config import Conversions as CV |
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from common.transformations.coordinates import LocalCoord, geodetic2ecef |
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LOOKAHEAD_TIME = 10. |
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MAPS_LOOKAHEAD_DISTANCE = 50 * LOOKAHEAD_TIME |
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def circle_through_points(p1, p2, p3): |
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"""Fits a circle through three points |
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Formulas from: http://www.ambrsoft.com/trigocalc/circle3d.htm""" |
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x1, y1, _ = p1 |
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x2, y2, _ = p2 |
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x3, y3, _ = p3 |
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A = x1 * (y2 - y3) - y1 * (x2 - x3) + x2 * y3 - x3 * y2 |
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B = (x1**2 + y1**2) * (y3 - y2) + (x2**2 + y2**2) * (y1 - y3) + (x3**2 + y3**2) * (y2 - y1) |
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C = (x1**2 + y1**2) * (x2 - x3) + (x2**2 + y2**2) * (x3 - x1) + (x3**2 + y3**2) * (x1 - x2) |
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D = (x1**2 + y1**2) * (x3 * y2 - x2 * y3) + (x2**2 + y2**2) * (x1 * y3 - x3 * y1) + (x3**2 + y3**2) * (x2 * y1 - x1 * y2) |
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return (-B / (2 * A), - C / (2 * A), np.sqrt((B**2 + C**2 - 4 * A * D) / (4 * A**2))) |
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def parse_speed_unit(max_speed): |
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"""Converts a maxspeed string to m/s based on the unit present in the input. |
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OpenStreetMap defaults to kph if no unit is present. """ |
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conversion = CV.KPH_TO_MS |
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if 'mph' in max_speed: |
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max_speed = max_speed.replace(' mph', '') |
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conversion = CV.MPH_TO_MS |
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return float(max_speed) * conversion |
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class Way: |
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def __init__(self, way): |
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self.id = way.id |
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self.way = way |
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points = list() |
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for node in self.way.get_nodes(resolve_missing=False): |
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points.append((float(node.lat), float(node.lon), 0.)) |
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self.points = np.asarray(points) |
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@classmethod |
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def closest(cls, query_results, lat, lon, heading): |
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results, tree, real_nodes, node_to_way = query_results |
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cur_pos = geodetic2ecef((lat, lon, 0)) |
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nodes = tree.query_ball_point(cur_pos, 500) |
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# If no nodes within 500m, choose closest one |
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if not nodes: |
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nodes = [tree.query(cur_pos)[1]] |
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ways = [] |
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for n in nodes: |
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real_node = real_nodes[n] |
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ways += node_to_way[real_node.id] |
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ways = set(ways) |
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closest_way = None |
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best_score = None |
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for way in ways: |
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way = Way(way) |
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points = way.points_in_car_frame(lat, lon, heading) |
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on_way = way.on_way(lat, lon, heading, points) |
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if not on_way: |
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continue |
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# Create mask of points in front and behind |
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x = points[:, 0] |
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y = points[:, 1] |
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angles = np.arctan2(y, x) |
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front = np.logical_and((-np.pi / 2) < angles, |
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angles < (np.pi / 2)) |
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behind = np.logical_not(front) |
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dists = np.linalg.norm(points, axis=1) |
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# Get closest point behind the car |
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dists_behind = np.copy(dists) |
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dists_behind[front] = np.NaN |
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closest_behind = points[np.nanargmin(dists_behind)] |
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# Get closest point in front of the car |
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dists_front = np.copy(dists) |
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dists_front[behind] = np.NaN |
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closest_front = points[np.nanargmin(dists_front)] |
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# fit line: y = a*x + b |
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x1, y1, _ = closest_behind |
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x2, y2, _ = closest_front |
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a = (y2 - y1) / max((x2 - x1), 1e-5) |
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b = y1 - a * x1 |
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# With a factor of 60 a 20m offset causes the same error as a 20 degree heading error |
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# (A 20 degree heading offset results in an a of about 1/3) |
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score = abs(a) * 60. + abs(b) |
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if closest_way is None or score < best_score: |
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closest_way = way |
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best_score = score |
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return closest_way |
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def __str__(self): |
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return "%s %s" % (self.id, self.way.tags) |
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@property |
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def max_speed(self): |
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"""Extracts the (conditional) speed limit from a way""" |
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if not self.way: |
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return None |
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tags = self.way.tags |
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max_speed = None |
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if 'maxspeed' in tags: |
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max_speed = parse_speed_unit(tags['maxspeed']) |
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if 'maxspeed:conditional' in tags: |
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max_speed_cond, cond = tags['maxspeed:conditional'].split(' @ ') |
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cond = cond[1:-1] |
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start, end = cond.split('-') |
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now = datetime.now() # TODO: Get time and timezone from gps fix so this will work correctly on replays |
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start = datetime.strptime(start, "%H:%M").replace(year=now.year, month=now.month, day=now.day) |
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end = datetime.strptime(end, "%H:%M").replace(year=now.year, month=now.month, day=now.day) |
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if start <= now <= end: |
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max_speed = parse_speed_unit(max_speed_cond) |
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return max_speed |
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def on_way(self, lat, lon, heading, points=None): |
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if points is None: |
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points = self.points_in_car_frame(lat, lon, heading) |
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x = points[:, 0] |
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return np.min(x) < 0. and np.max(x) > 0. |
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def closest_point(self, lat, lon, heading, points=None): |
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if points is None: |
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points = self.points_in_car_frame(lat, lon, heading) |
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i = np.argmin(np.linalg.norm(points, axis=1)) |
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return points[i] |
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def distance_to_closest_node(self, lat, lon, heading, points=None): |
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if points is None: |
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points = self.points_in_car_frame(lat, lon, heading) |
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return np.min(np.linalg.norm(points, axis=1)) |
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def points_in_car_frame(self, lat, lon, heading): |
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lc = LocalCoord.from_geodetic([lat, lon, 0.]) |
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# Build rotation matrix |
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heading = math.radians(-heading + 90) |
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c, s = np.cos(heading), np.sin(heading) |
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rot = np.array([[c, s, 0.], [-s, c, 0.], [0., 0., 1.]]) |
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# Convert to local coordinates |
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points_carframe = lc.geodetic2ned(self.points).T |
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# Rotate with heading of car |
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points_carframe = np.dot(rot, points_carframe[(1, 0, 2), :]).T |
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return points_carframe |
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def next_way(self, query_results, lat, lon, heading, backwards=False): |
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results, tree, real_nodes, node_to_way = query_results |
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if backwards: |
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node = self.way.nodes[0] |
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else: |
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node = self.way.nodes[-1] |
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ways = node_to_way[node.id] |
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way = None |
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try: |
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# Simple heuristic to find next way |
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ways = [w for w in ways if w.id != self.id and w.tags['highway'] == self.way.tags['highway']] |
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if len(ways) == 1: |
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way = Way(ways[0]) |
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except KeyError: |
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pass |
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return way |
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def get_lookahead(self, query_results, lat, lon, heading, lookahead): |
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pnts = None |
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way = self |
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valid = False |
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for i in range(5): |
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# Get new points and append to list |
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new_pnts = way.points_in_car_frame(lat, lon, heading) |
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if pnts is None: |
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pnts = new_pnts |
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else: |
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pnts = np.vstack([pnts, new_pnts]) |
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# Check current lookahead distance |
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max_dist = np.linalg.norm(pnts[-1, :]) |
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if max_dist > lookahead: |
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valid = True |
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if max_dist > 2 * lookahead: |
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break |
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# Find next way |
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way = way.next_way(query_results, lat, lon, heading) |
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if not way: |
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break |
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return pnts, valid |
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