Autonomous Vehicle Trajectory Planning for Speed-Reducing Requests
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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) to identify target stopping locations in real-time, 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 functional modules: sensor data acquisition, factor evaluation (urgency, road environment, traffic conditions), target location identification, and trajectory planning. Each module processes specific aspects independently, reducing overall computational complexity while maintaining comprehensive safety evaluation
Solution Approach 2:
The system performs preliminary evaluation of potential stopping locations and factors before final decision-making. By pre-assessing road environments, traffic conditions, and candidate locations, the system prepares multiple options in advance, enabling faster real-time responses without compromising safety
2Adaptability or versatility
If the AV system continuously updates the trajectory plan and target location based on changing conditions, then the adaptability to dynamic road environments is improved, but the loss of time for repeated evaluation and planning increases
Solution Approach 1:
The trajectory planning system is designed to be dynamic and adaptive, continuously adjusting the target location and path based on real-time sensor data and changing road conditions. The system monitors environmental changes and updates plans only when necessary, balancing adaptability with time efficiency
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the dynamic environment is constantly monitored and fed back into the planning system. This enables the AV to detect changes in road conditions, traffic, or obstacles and adjust the trajectory plan accordingly, maintaining adaptability while minimizing unnecessary re-planning cycles
3Manufacturing precision
If the AV system sets a minimum quality threshold for target locations based on multiple factors, then the manufacturing precision of the stopping decision is improved, but the difficulty of detecting and measuring suitable locations increases
Solution Approach 1:
The system transforms the complex multi-factor quality assessment into quantifiable parameters with defined thresholds. By converting qualitative factors (road suitability, safety conditions) into measurable parameters, the system can automatically evaluate and compare potential stopping locations against minimum quality thresholds, improving decision precision while reducing detection difficulty
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.