Autonomous Vehicle Unprotected Left Turn Decision Logic
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Solution Overview
Problem
Current autonomous vehicle control algorithms are not optimized for determining when to commence an unprotected left turn maneuver, which can lead to inefficiencies and potential safety issues.
Innovation Solution
A processor-implemented method and system that determine the vehicle's zone (stopping, dilemma, or cross-traffic zone) and assess traffic signals and approaching vehicles to decide when it is safe to perform an unprotected left turn, using GPS, camera, and lidar data to predict vehicle movement and traffic signal timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses traditional control algorithms for unprotected left turns, then the system complexity is low, but the safety and decision-making accuracy deteriorate
Solution Approach 1:
The patent segments the road into three distinct zones (stopping zone, dilemma zone, and cross-traffic zone) based on vehicle position relative to the intersection. Each zone has specific decision-making rules for unprotected left turns, allowing the system to provide optimized safety guidance for each segment rather than using a single complex algorithm for all scenarios.
Solution Approach 2:
The system performs preliminary classification of the vehicle's zone before making turn decisions. By determining which zone the vehicle is in first, the system can then apply the appropriate decision-making criteria for that specific zone, improving safety without requiring a single overly complex algorithm to handle all possible scenarios.
2Reliability
If the autonomous vehicle waits for complete traffic clearance before turning, then safety is improved, but the productivity and navigation efficiency deteriorate
Solution Approach 1:
The patent divides the decision-making process into zone-specific rules that allow the vehicle to turn when safe in the cross-traffic zone, while requiring more caution in the stopping and dilemma zones. This segmentation enables efficient navigation when conditions permit while maintaining safety standards.
Solution Approach 2:
The system applies different levels of caution based on the zone - full clearance verification in stopping zone, partial verification in dilemma zone, and more flexible criteria in cross-traffic zone. This partial action approach allows the vehicle to turn more efficiently when in zones where the risk is lower, improving overall navigation efficiency while maintaining safety.
Data Source
AI summary
A method in an autonomous vehicle comprises determining to perform a left turn maneuver when the vehicle is in a stopping zone, the vehicle is clear of approaching vehicles, and a relevant traffic signal displays a go signal. The method further comprises determining to perform the left turn maneuver when the vehicle has entered a dilemma zone, the vehicle is clear of approaching vehicles, and the relevant traffic signal displays a go signal, a caution signal, or has displayed a stop signal for less than a predetermined amount of time. The method further comprises determining to perform the left turn maneuver when the vehicle has entered a cross-traffic zone, the vehicle is clear of approaching vehicles, and the relevant traffic signal displays a go signal, a caution signal, or a stop signal.