Traffic Signal Anomaly Detection for Autonomous Intersections
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Solution Overview
Problem
Autonomous vehicles face challenges in safely navigating intersections with malfunctioning traffic signals, which can lead to unsafe conditions and inability to proceed due to unclear or inconsistent signal states.
Innovation Solution
The system employs a traffic signal detection system that utilizes multiple inputs, including camera images and sensor data, to detect anomalies in traffic signals by applying heuristics and rules, classifies the anomalies, and adjusts vehicle control strategies accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses standard traffic signal detection methods, then the system can operate with simple detection logic, but the vehicle cannot reliably detect malfunctioning traffic signals leading to unsafe conditions
Solution Approach 1:
The detection system is divided into multiple independent modules: a first module detects traffic signal states using standard methods, while a second module detects anomaly indicators such as vehicle stopping behaviors and pedestrian actions. These segmented modules work together to comprehensively identify malfunctioning signals without requiring complete system redesign.
Solution Approach 2:
The anomaly detection module is nested within the existing traffic signal detection system. The second module's anomaly detection capabilities are integrated into the overall detection framework, allowing the system to perform both standard detection and anomaly detection through a unified structure.
2Reliability
If the vehicle stops at every potential anomaly to ensure safety, then safety is improved, but the vehicle cannot proceed efficiently causing loss of time
Solution Approach 1:
The system continuously monitors multiple indicators including traffic signal states, vehicle behaviors, and pedestrian actions to provide feedback on the actual traffic condition. This feedback loop allows the vehicle to dynamically adjust its decision-making, stopping only when genuine anomalies are confirmed rather than at every potential issue.
Solution Approach 2:
Instead of always stopping at intersections with detected anomalies, the system applies partial action by stopping only when the anomaly classification reaches a certain threshold of confidence. For lower-confidence detections, the system continues with caution rather than complete stopping, reducing unnecessary time loss while maintaining safety.
3Measurement precision
If the system uses multiple detection inputs and complex anomaly classification, then detection accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system pre-establishes a classification framework with defined anomaly types and detection criteria before runtime. By preparing the classification structure in advance, the system can quickly match observed anomalies to predefined categories during real-time operation, reducing processing time while maintaining high detection 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.