Autonomous Vehicle Traffic Signal Anomaly Response at Intersections
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Solution Overview
Problem
Autonomous vehicles face challenges in safely navigating through intersections with malfunctioning traffic signals, as existing systems struggle to differentiate between temporary and permanent malfunctions, and may become stuck or unable to proceed due to insufficient information from traffic signal detection systems.
Innovation Solution
The method involves detecting anomalies in traffic signals using a combination of sensor data and heuristics, classifying the anomalies, and responding appropriately by accessing a mapping of anomaly classifications to vehicle responses, which includes controlling the vehicle to stop, proceed, or request assistance based on the classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses standard traffic signal detection systems, then the vehicle can detect normal traffic signals, but the vehicle cannot accurately differentiate between temporary and permanent malfunctions of traffic signals
Solution Approach 1:
The patent segments the traffic signal detection process into multiple independent analysis components: (1) direct traffic signal state detection, (2) cross-traffic behavior analysis, (3) temporal pattern recognition, and (4) anomaly classification. Each component processes specific aspects of traffic signal behavior independently, allowing the system to differentiate between temporary occlusions and permanent malfunctions by comparing results across segments.
Solution Approach 2:
The system implements feedback loops where detected traffic signal states and observed vehicle behaviors are continuously fed back into the anomaly detection algorithm. The system monitors whether detected patterns are consistent with expected traffic signal operation over time, and adjusts its classification of signal status based on accumulated evidence from multiple observation cycles.
2Reliability
If the autonomous vehicle stops at every detected anomaly, then the vehicle ensures safety, but the vehicle reduces operational efficiency and may become stuck at intersections
Solution Approach 1:
The patent implements dynamic response selection where the vehicle's action at an intersection is adjusted based on the classified anomaly type. For temporary anomalies with high confidence, the vehicle proceeds without stopping. For ambiguous or high-severity anomalies, the vehicle stops or requests remote assistance. This dynamic approach allows the system to optimize between safety and efficiency based on real-time anomaly characteristics.
Solution Approach 2:
The system changes the operational parameter of stopping distance and waiting time based on anomaly classification results. For low-severity anomalies, the vehicle may proceed with normal stopping distance. For high-severity anomalies, the vehicle increases stopping distance and waiting time, or transitions to remote assistance mode, thereby adapting safety measures to the specific anomaly situation rather than applying a fixed stopping rule.
3Measurement precision
If the autonomous vehicle uses multiple detection methods and heuristics, then the vehicle can classify anomalies more accurately, but the vehicle increases computational complexity and processing time
Solution Approach 1:
The patent performs preliminary classification of traffic signal anomalies using a hierarchy of detection methods. The system first applies simple, computationally efficient checks (such as basic signal state validation and obvious pattern mismatches) to quickly identify and resolve clear-cut cases. Only when these preliminary methods are inconclusive does the system engage more complex heuristic analysis and multiple detection modalities, thereby reducing overall computational burden while maintaining high classification accuracy.
Data Source
AI summary
Aspects of the disclosure relate to detecting and responding to malfunctioning traffic signals for a vehicle having an autonomous driving mode. For instance, information identifying a detected state of a traffic signal for an intersection. An anomaly for the traffic signal may be detected based on the detected state and prestored information about expected states of the traffic signal. The vehicle may be controlled in the autonomous driving mode based on the detected anomaly.