Traffic Signal Detection With Confidence Checks for Autonomous Vehicles
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Solution Overview
Problem
Existing autonomous vehicles face challenges in accurately detecting and interpreting traffic signals, especially in unexpected locations or when signals are temporarily or permanently moved.
Innovation Solution
The method involves scanning a target area using vehicle sensors to obtain information about traffic signals, determining their location, state, and confidence level by comparing to known locations and patterns, and using this information to control the vehicle in autonomous mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous vehicles use sensor-based detection methods to identify traffic signals, then they can operate without driver input, but they face challenges in accurately detecting traffic signals in unexpected locations or when signals are temporarily moved
Solution Approach 1:
The system pre-scans and identifies traffic signal locations and characteristics before the vehicle reaches the intersection. By performing detection and classification of traffic signals in advance, the system builds a database of expected signal locations, colors, and patterns, enabling more reliable autonomous decision-making when the vehicle approaches the intersection.
Solution Approach 2:
The system continuously monitors and compares detected traffic signals against expected patterns and locations. When discrepancies are found (such as temporarily moved signals), the system adjusts its detection parameters and uses feedback from multiple sensors to confirm the actual signal state, improving detection accuracy in dynamic environments.
2Adaptability or versatility
If the vehicle scans a wide target area to detect traffic signals in unexpected locations, then detection coverage improves, but the complexity of processing target area information increases
Solution Approach 1:
The wide target area is divided into multiple sub-regions or zones of interest. Each region is scanned and processed independently, allowing the system to manage complex detection tasks in smaller, more manageable units. This segmentation reduces the overall processing complexity while maintaining comprehensive coverage of the entire target area.
Solution Approach 2:
Different processing strategies are applied to different regions of the target area based on their characteristics. High-priority regions (such as areas where traffic signals are most likely to be located) receive more intensive processing, while lower-priority regions use simplified detection methods, optimizing the balance between coverage and processing complexity.
3Reliability
If the vehicle uses multiple sensors to improve traffic signal detection reliability, then detection accuracy in various conditions improves, but the system complexity and energy consumption increase
Solution Approach 1:
The sensor system is designed with multi-functional sensors that can perform multiple detection tasks. For example, cameras are used for both traffic signal detection and general environment monitoring, while LIDAR serves both distance measurement and object classification purposes. This reduces the total number of sensors needed while maintaining high detection reliability.
Solution Approach 2:
The sensor system includes self-calibration and self-diagnosis capabilities that allow sensors to automatically adjust their parameters and detect their own status without external intervention. This reduces the operational complexity of managing multiple sensors and ensures they maintain optimal performance for reliable traffic signal detection.
4Reliability
If the vehicle determines confidence levels by comparing detected signals to known locations, then detection reliability improves, but the time required for verification increases
Solution Approach 1:
The system performs partial verification by comparing detected signals against known locations and patterns, but only to the extent necessary to establish sufficient confidence. Rather than requiring complete verification of all parameters, the system uses heuristic rules to determine when partial matching is adequate, reducing verification time while maintaining acceptable reliability levels.
Solution Approach 2:
The system pre-loads and stores known traffic signal locations, patterns, and characteristics in advance. When a traffic signal is detected, the system quickly compares it against this pre-prepared database rather than performing full verification from scratch, significantly reducing the time required for confidence assessment while maintaining high reliability.
Data Source
AI summary
Methods and devices for detecting traffic signals and their associated states are disclosed. In one embodiment, an example method includes a scanning a target area using one or more sensors of a vehicle to obtain target area information. The vehicle may be configured to operate in an autonomous mode, and the target area may be a type of area where traffic signals are typically located. The method may also include detecting a traffic signal in the target area information, determining a location of the traffic signal, and determining a state of the traffic signal. Also, a confidence in the traffic signal may be determined. For example, the location of the traffic signal may be compared to known locations of traffic signals. Based on the state of the traffic signal and the confidence in the traffic signal, the vehicle may be controlled in the autonomous mode.