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173 lines
6.0 KiB
173 lines
6.0 KiB
"""
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Copyright (c) 2025, Rick Lan
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, and/or sublicense,
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for non-commercial purposes only, subject to the following conditions:
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- The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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- Commercial use (e.g. use in a product, service, or activity intended to
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generate revenue) is prohibited without explicit written permission from
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the copyright holder.
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THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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"""
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from opendbc.car.interfaces import RadarInterfaceBase
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from opendbc.can.parser import CANParser
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from opendbc.car.structs import RadarData
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from typing import List, Tuple
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# car head to radar
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DREL_OFFSET = -1.35
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# max object amount will process
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MAX_OBJECTS = 100
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# lat distance, typically max lane width is 3.7m
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MAX_LAT_DIST = 3.6
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# objects to ignore thats really close to the vehicle (after DREL_OFFSET applied)
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MIN_DIST = 2.5
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# when a object has really large negative v_rel means its stationary / standstill
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# so with the values below (v_rel = -10, lat_dist = 2.), we are trying to ignore:
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# when the ego vehicle is driving above 36 km/h (22.37 mph), we will ignore objects that lateral distance is above 2m on left or right.
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STATIONARY_OBJ_VREL = -10.
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STATIONARY_OBJ_LAT_DIST = 2.
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# when we detect an object that's really closed to the ego vehicle
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# we ignore the objects that's away from left or right
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CLOSED_OBJ_DREL = 10
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CLOSED_OBJ_YREL = 2.
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# ignore objects that has small radar cross sections (-64 ~ 63.5)
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MIN_RCS = -5.
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# ignore oncoming objects
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IGNORE_OBJ_STATE = 2
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# ignore objects that we haven't seen for 5 secs
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NOT_SEEN_INIT = 33*5
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def _create_radar_parser():
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messages = [("Status", float('nan')), ("ObjectData", float('nan'))]
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messages += [(f"ObjectData_{i}", float('nan')) for i in range(MAX_OBJECTS)]
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return CANParser('u_radar', messages, 1)
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class RadarInterface(RadarInterfaceBase):
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def __init__(self, CP):
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super().__init__(CP)
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self.updated_messages = set()
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self.rcp = _create_radar_parser()
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self._pts_cache = dict()
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self._pts_not_seen = {key: 0 for key in range(255)}
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self._should_clear_cache = False
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def _create_parsable_object_can_strings(self, can_strings: List[Tuple]) -> Tuple[List[Tuple], int]:
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"""Optimized object string parsing with minimal allocations."""
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if not can_strings or not isinstance(can_strings[0], tuple) or len(can_strings[0]) < 2:
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return [], 0
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# Pre-allocate list with known maximum size
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new_list = []
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new_list_append = new_list.append # Local reference for faster access
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records = can_strings[0][1]
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id_num = 1
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for record in records:
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if id_num > MAX_OBJECTS:
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break
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if record[0] == 0x60B:
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new_list_append((id_num + 383, record[1], record[2]))
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id_num += 1
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return [(can_strings[0][0], new_list)], len(new_list)
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# called by card.py, 100hz
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def update(self, can_strings):
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vls = self.rcp.update(can_strings)
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self.updated_messages.update(vls)
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if 1546 in self.updated_messages:
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self._should_clear_cache = True
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if 1547 in self.updated_messages:
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parsable_can_string, size = self._create_parsable_object_can_strings(can_strings)
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self.rcp.update(parsable_can_string)
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# clean cache when we see a 0x60a then a 0x60b
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if self._should_clear_cache:
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self._pts_cache.clear()
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self._should_clear_cache = False
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for i in range(size):
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cpt = self.rcp.vl[f'ObjectData_{i}']
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track_id = int(cpt['ID'])
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d_rel = float(cpt['DistLong']) + DREL_OFFSET
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y_rel = -float(cpt['DistLat'])
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obj_class = int(cpt['Class'])
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# ignore oncoming objects
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if int(cpt['DynProp']) == IGNORE_OBJ_STATE:
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continue
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# only apply filters below when object is a point (0) not a vehicle (1)
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if obj_class == 0:
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# ignore really closed objects
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if d_rel < MIN_DIST:
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continue
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# ignore objects with really small radar cross sections
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if float(cpt['RCS']) < MIN_RCS:
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continue
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# ignore far left/right objects
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if abs(y_rel) > MAX_LAT_DIST:
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continue
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# ignore closed left/right objects when closed
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if d_rel < CLOSED_OBJ_DREL and abs(y_rel) > CLOSED_OBJ_YREL:
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continue
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# add to cache
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if track_id not in self._pts_cache:
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self._pts_cache[track_id] = RadarData.RadarPoint()
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self._pts_cache[track_id].trackId = track_id
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self._pts_not_seen[track_id] = NOT_SEEN_INIT
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self._pts_cache[track_id].yvRel = float(cpt['VRelLat'])
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self._pts_cache[track_id].dRel = d_rel
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self._pts_cache[track_id].yRel = y_rel
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self._pts_cache[track_id].vRel = float(cpt['VRelLong'])
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self._pts_cache[track_id].aRel = float('nan')
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self._pts_cache[track_id].measured = True
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self.updated_messages.clear()
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if self.frame % 2 == 0:
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keys_to_remove = [key for key in self.pts if key not in self._pts_cache]
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for key in keys_to_remove:
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self._pts_not_seen[key] -= 1
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if self._pts_not_seen[key] <= 0:
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del self.pts[key]
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self.pts.update(self._pts_cache)
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ret = RadarData()
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if not self.rcp.can_valid:
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ret.errors.canError = True
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ret.points = list(self.pts.values())
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return ret
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return None
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