Autonomous Vehicle Trajectory Planning for Speed-Reducing Maneuvers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles (AVs) face challenges in efficiently responding to signals requesting speed-reducing maneuvers, such as stopping, due to the need to evaluate various factors like urgency, road environment, and available stopping locations in real-time, while adapting to changing conditions during trajectory planning.
Innovation Solution
The AV system processes signals to identify and adapt a target stopping location by analyzing data from user interfaces, sensors, and maps, continuously updating the trajectory based on observed data, and adjusting plans if the initial location becomes unavailable, using algorithms to evaluate quality and apply minimum quality thresholds to ensure safe and efficient execution of speed-reducing maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AV system evaluates multiple factors (urgency, road environment, stopping locations) in real-time to identify target stopping locations, then the safety and reliability of speed-reducing maneuvers is improved, but the computational complexity and processing time increase
Solution Approach 1:
The AV system segments the complex decision-making process into distinct modules: receiving request signals, evaluating road environment factors, identifying candidate stopping locations, selecting target locations, and planning trajectories. This modular segmentation allows each component to be optimized independently while maintaining overall system reliability for safety-critical maneuvers.
Solution Approach 2:
The system performs preliminary evaluation of road environment factors and candidate stopping locations before actual maneuver execution. By pre-assessing multiple potential stopping locations and their suitability based on urgency and safety criteria, the system reduces real-time computational burden while maintaining high reliability in selecting appropriate target locations.
2Adaptability or versatility
If the AV system continuously updates trajectory plans based on changing conditions, then the adaptability to dynamic road environments is improved, but the processing time and computational load increase
Solution Approach 1:
The trajectory planning system implements dynamic replanning capabilities that adapt to changing road conditions, traffic patterns, and vehicle states. The system continuously monitors environmental factors and updates trajectory plans in real-time, allowing the AV to respond flexibly to dynamic conditions while maintaining efficient processing through optimized update frequencies and conditional triggering.
Solution Approach 2:
The system employs feedback mechanisms where trajectory execution status is continuously monitored and fed back to the planning module. This feedback loop enables the system to detect when conditions change significantly and trigger selective trajectory updates only when necessary, balancing adaptability with processing efficiency by avoiding unnecessary recomputations.
3Measurement precision
If the AV system applies quality thresholds and evaluates multiple candidate locations, then the precision in selecting appropriate stopping locations is improved, but the computational effort and time required increase
Solution Approach 1:
The system applies different quality evaluation criteria and thresholds to different candidate stopping locations based on local characteristics such as road type, traffic density, and urgency level. By tailoring the evaluation rigor to each location's specific context, the system achieves high precision in selecting appropriate stopping places while avoiding uniform over-evaluation that would reduce computational efficiency.
Solution Approach 2:
The quality threshold parameters for stopping location evaluation are dynamically adjusted based on the urgency of the speed-reducing maneuver and current road conditions. For high-urgency situations, the system may reduce threshold stringency to enable faster decision-making, while for less urgent scenarios, more rigorous evaluation can be applied, thus balancing precision with computational productivity.
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.