Secondary Perception for Collision Trajectory Validation
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Solution Overview
Problem
Existing autonomous vehicle collision avoidance systems face challenges in accurately validating trajectories to prevent collisions, particularly due to errors in perception and trajectory generation, which can lead to unsafe maneuvers and increased computational resource usage.
Innovation Solution
A collision avoidance system that employs a secondary perception component with machine-learning models to classify objects and generate occupancy maps, combining sensor data from different types to validate trajectories and initiate alternative maneuvers if necessary, thereby reducing errors and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a primary perception component generates trajectories based on all sensor data, then the vehicle can operate autonomously, but errors in perception can lead to invalid trajectories and collisions
Solution Approach 1:
A secondary perception component is introduced as an intermediary between the primary perception component and the trajectory execution. This secondary component independently validates trajectories by generating its own occupancy maps from sensor data, acting as a mediator to catch errors that the primary system might miss, thereby improving reliability without relying solely on the primary system's precision.
Solution Approach 2:
The system implements a feedback mechanism where the secondary perception component continuously monitors and validates the primary system's trajectory decisions. When the secondary system detects an invalid trajectory or occupancy conflict, it provides feedback to reject or correct the trajectory, creating a closed-loop safety verification system that improves overall reliability.
2Measurement precision
If multiple sensors and systems are used to detect and track objects, then collision detection accuracy improves, but computational resource usage increases
Solution Approach 1:
The perception system is segmented into two independent components: a primary perception system for generating trajectories and a secondary perception system for validating them. Each component processes sensor data independently to create its own occupancy maps, allowing the system to distribute computational load and verify safety without requiring one system to process all data at maximum intensity.
Solution Approach 2:
The secondary perception component performs partial perception actions - it doesn't need to detect all objects with perfect accuracy, but only needs to detect enough objects to validate or reject the primary system's trajectory. This partial action approach reduces computational requirements while still providing safety verification.
3Reliability
If the system validates all trajectories before execution, then safety improves, but response time may be delayed
Solution Approach 1:
The secondary perception component continuously prepares occupancy maps and validation criteria in advance before trajectories need to be executed. By maintaining pre-computed occupancy information and validation rules ready, the system can perform rapid validation when trajectories are generated, reducing the actual validation time while maintaining thorough safety checks.
Data Source
AI summary
A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may receive sensor data, receive and/or determine a corridor associated with operation of a vehicle, classify a portion of the sensor data associated with the corridor as either ground or an object, determine a position and/or velocity of at least the nearest object, determine a threshold distance associated with the vehicle, and control the vehicle based at least in part on the position and/or velocity of the nearest object and the threshold distance.