Autonomous Vehicle Speed-Reducing Maneuver Planning
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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
An AV system that processes signals to identify and adapt a target stopping location by analyzing data on request features, road conditions, and sensor data, continuously updating the trajectory plan and selecting an appropriate location based on quality thresholds and availability, ensuring 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, sensor data) 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 from multiple sources, evaluating road environment factors, analyzing sensor data, identifying candidate stopping locations, and selecting target locations. This modular segmentation allows each component to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
The system performs preliminary evaluation of road environment and sensor data to pre-identify candidate stopping locations before a stop request is made. This preliminary action prepares the system in advance, reducing the computational burden during actual emergency stopping situations while maintaining high safety standards.
2Adaptability or versatility
If the AV system continuously updates trajectory plans and target locations during execution, then the adaptability to changing road conditions is improved, but the loss of time for maneuver execution increases
Solution Approach 1:
The system implements continuous feedback loops during trajectory execution, monitoring road conditions and sensor data to determine when updates to target locations are necessary. This feedback mechanism allows the system to adapt to changing conditions only when required, minimizing unnecessary computational cycles and time loss while maintaining high adaptability.
Solution Approach 2:
The AV system performs periodic evaluations of target location validity during trajectory execution rather than continuous updates. This periodic action reduces computational overhead and time loss while still maintaining adequate adaptability to significant changes in road conditions or environment.
3Manufacturing precision
If the AV system considers multiple candidate target locations and quality thresholds, then the manufacturing precision of stopping location selection is improved, but the loss of time in selecting optimal location increases
Solution Approach 1:
The system applies quality thresholds and evaluation criteria specific to different stopping location characteristics (e.g., proximity to intersections, road shoulder availability, pedestrian activity). This local quality approach allows the system to make precise selections by evaluating locations against context-specific criteria rather than applying uniform standards, reducing overall selection time while maintaining high precision.
Solution Approach 2:
The AV system dynamically adjusts quality thresholds and evaluation parameters based on the urgency of the stop request and current road conditions. In emergency situations, thresholds are lowered to enable faster selection, while in less critical situations, higher precision criteria can be applied. This parameter adaptation balances precision requirements with time constraints.
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.