Autonomous Vehicle Trajectory Replanning for Safe Speed-Reducing Stops
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Solution Overview
Problem
Autonomous vehicles (AVs) face challenges in efficiently identifying and adapting to target locations for speed-reducing maneuvers, such as stopping, especially when initial locations become unavailable or unsuitable during execution, due to the need for real-time data processing and dynamic trajectory planning.
Innovation Solution
The AV system analyzes various data sources, including user inputs, sensor data, and map data, to evaluate the quality of potential target locations and adapt its trajectory plan based on factors like urgency, traffic conditions, and availability, continuously updating the target location until the maneuver is completed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AV system continuously analyzes multiple data sources and dynamically updates target locations in real-time, then the safety and adaptability of speed-reducing maneuvers is improved, but the computational complexity and processing time increase
Solution Approach 1:
The trajectory planning process is divided into discrete evaluation cycles that continuously reassess target location quality based on updated sensor data, map data, and traffic conditions. This segmentation allows the system to manage computational complexity by processing decisions in manageable intervals rather than requiring simultaneous analysis of all parameters.
Solution Approach 2:
The system dynamically adjusts the target location selection based on real-time changes in environmental conditions, sensor data quality, and traffic状况. The trajectory plan is not static but continuously adapted, allowing the AV to respond to changing conditions while maintaining safety through iterative refinement rather than complex upfront planning.
2Productivity
If the AV system evaluates multiple factors including urgency, traffic conditions, and location quality to select target locations, then the effectiveness of maneuver execution is improved, but the time required for decision-making increases
Solution Approach 1:
The system implements continuous feedback loops where sensor data, map data, and traffic information are constantly monitored and used to reassess target location quality. This feedback mechanism enables the AV to make efficient decisions by building on previous evaluations rather than starting from scratch, reducing decision-making time while maintaining comprehensive factor analysis.
Solution Approach 2:
The system pre-evaluates potential target locations and maintains a ready list of candidate stopping places based on map data and historical traffic patterns. When a speed-reducing maneuver is initiated, the system can quickly select from pre-vetted options rather than performing full evaluations in real-time, reducing decision-making latency while maintaining effectiveness.
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.