Vehicle Collision Prediction Using Path Polygons and Agent Trajectories
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Solution Overview
Problem
Traditional collision avoidance systems in vehicles often cause unnecessary vehicle yield and traffic delays by simply identifying the presence of surfaces, leading to unnatural and inefficient navigation.
Innovation Solution
A vehicle computing system determines potential collision zones by analyzing path polygons and agent trajectories, applying time-space overlap and probability density functions to predict collision likelihood, and adjusts vehicle actions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional collision avoidance systems identify the presence of surfaces and adjust vehicle velocity to avoid collision, then collision avoidance is achieved, but unnecessary vehicle yield and traffic delays occur
Solution Approach 1:
The system changes the parameters of collision detection from simple surface presence to complex trajectory analysis. By computing path polygons, agent trajectories, time-space overlaps, and probability density functions, the system transforms basic collision detection into a sophisticated prediction system that distinguishes between actual and potential collisions, thereby reducing unnecessary yield while maintaining reliable collision avoidance
Solution Approach 2:
The system performs preliminary actions by predicting potential collision zones before actual collision occurs. By analyzing current paths and velocities of multiple agents, computing possible collision zones, and determining time-space overlaps in advance, the system can make informed decisions about whether to yield or maintain course, avoiding unnecessary traffic delays while ensuring safety
2Reliability
If traditional collision avoidance systems adjust vehicle velocity to avoid all detected surfaces, then collision safety is improved, but natural and efficient navigation is reduced
Solution Approach 1:
The system applies local quality by treating different spatial regions and agent types differently. Instead of uniform collision avoidance for all surfaces, the system computes individual path polygons for the vehicle and each agent, calculates specific time-space overlaps for each agent-vehicle pair, and determines collision probabilities locally for each potential collision zone, enabling natural navigation while maintaining safety
Solution Approach 2:
The system implements dynamics by continuously updating trajectory predictions based on current agent positions and velocities. By recalculating path polygons, agent trajectories, and collision zones in real-time, the system adapts to changing environmental conditions, allowing natural and efficient navigation while maintaining collision safety through dynamic adjustment of avoidance maneuvers
Data Source
AI summary
A vehicle computing system may implement techniques to control a vehicle to avoid collisions between the vehicle and agents (e.g., dynamic objects) in an environment. The techniques may include generating a representation of a path of the vehicle through an environment as a polygon. The vehicle computing system may compare the two-dimensional path with a trajectory of an agent determined using sensor data to determine a collision zone between the vehicle and the agent. The vehicle computing system may determine a risk of collision based on predicted velocities and probable accelerations of the vehicle and the agent approaching and traveling through the collision zone. Based at least in part on the risk of collision, the vehicle computing system may cause the vehicle to perform an action.