Autonomous Vehicle Stop-Request Trajectory Replanning
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Solution Overview
Problem
Autonomous vehicles face challenges in responding effectively to requests for immediate or speed-reducing maneuvers, particularly in identifying suitable target locations and adapting trajectory plans in dynamic environments.
Innovation Solution
The system enables autonomous vehicles to analyze various data sources, including sensor data, map data, and user inputs, to identify and adapt to target locations for speed-reducing maneuvers, such as stopping, by evaluating factors like urgency, quality, and availability, and continuously updating the trajectory plan based on real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the AV system immediately executes a stop request signal, then the response time is reduced, but the safety and appropriateness of the stopping location may be compromised
Solution Approach 1:
The system pre-identifies and evaluates multiple candidate target locations for potential stopping maneuvers before a stop request is made. When a stop request signal is received, the system can immediately select from pre-evaluated candidates, reducing response time while maintaining safety through prior analysis of location suitability factors.
2Reliability
If the AV system evaluates multiple factors and continuously updates trajectory plans, then the safety and accuracy of maneuver execution is improved, but the computational complexity and processing time increases
Solution Approach 1:
The system divides the complex decision-making process into distinct modular components: candidate location identification, factor evaluation (safety, legality, suitability), trajectory planning, and real-time adjustment. Each module processes specific aspects independently, reducing overall computational complexity while maintaining comprehensive evaluation through structured segmentation of the problem space.
3Device complexity
If the AV system maintains a fixed trajectory plan, then the computational load is reduced, but the system cannot adapt to changing road conditions or unavailable target locations
Solution Approach 1:
The system implements dynamic trajectory planning where the trajectory plan is continuously updated based on real-time sensor data, changing road conditions, and availability of target locations. The system monitors environmental factors and automatically adjusts the trajectory plan to maintain safety and achieve the stopping objective, transitioning from static to dynamic adaptation.
4Reliability
If the AV system considers multiple target locations and continuously updates status, then the reliability of maneuver completion is improved, but the time and computational resources required increase
Solution Approach 1:
The system evaluates multiple candidate target locations but implements prioritization and threshold-based selection. Instead of exhaustively analyzing all possible locations, the system identifies a limited set of high-priority candidates meeting minimum criteria, selects the best option, and proceeds with execution. This partial evaluation approach achieves sufficient reliability without the time cost of complete exhaustive analysis.
Data Source
AI summary
Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.