Vehicle Collision Prediction Filters for False Positive Reduction
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Solution Overview
Problem
Autonomous and semi-autonomous vehicle planning systems face challenges in accurately predicting and avoiding collisions due to the complexity of perception systems, often resulting in false positives and inefficient use of computational resources.
Innovation Solution
A vehicle safety system implements a model that uses a rear collision filter, distance filter, and report time filter to determine intersection probabilities, reducing false positives by selectively reporting potential collisions and optimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If perception systems utilize sensor data to detect objects and generate planning actions, then vehicle safety and collision avoidance are improved, but system complexity increases making reliability inspection difficult
Solution Approach 1:
The system segments the complex perception and planning process into distinct components: object detection module, trajectory prediction module, collision risk assessment module, and action generation module. Each module handles a specific aspect of collision avoidance, making the overall system more inspectable and reliable while maintaining comprehensive safety coverage.
Solution Approach 2:
A planning policy module acts as an intermediary between perception systems and vehicle control, standardizing the interface and decision-making process. This intermediary layer simplifies reliability inspection by providing a clear boundary between complex perception processing and actionable control decisions, while maintaining the integrity of collision avoidance functionality.
2Reliability
If the system generates actions based on detected objects to avoid collisions, then safety is improved, but false positives increase due to system complexity
Solution Approach 1:
The system performs preliminary trajectory prediction and collision risk assessment before generating final avoidance actions. By pre-evaluating potential collision scenarios and filtering out low-risk situations early in the processing chain, the system reduces false positives while maintaining accurate detection of genuine collision threats.
Solution Approach 2:
The system incorporates feedback mechanisms where collision risk assessment results feed back into trajectory prediction and object detection prioritization. This feedback loop allows the system to adjust its sensitivity and focus computational resources on high-risk scenarios, reducing false positives while maintaining high detection accuracy for actual threats.
Data Source
AI summary
Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a rear collision filter, a distance filter, and a time to stop filter to determine whether a predicted collision may be a false positive, in which case the techniques may include refraining from reporting such predicted collision to other another vehicle computing device to control the vehicle.