Autonomous Vehicle Yield Planning with Wait-State Trajectories
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Solution Overview
Problem
Conventional autonomous vehicles are unable to safely and predictably navigate yield scenarios, such as intersections and merging lanes, as they fail to encode and deploy yielding protocols, leading to unpredictable behavior and potential collisions.
Innovation Solution
The implementation of a yield planner system that analyzes sensor data to identify yield scenarios and determines appropriate yielding behavior by generating trajectories for both the autonomous vehicle and other contenders, evaluating potential conflicts, and selecting a yielding strategy to ensure safe and polite navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autonomous vehicles avoid collisions by yielding indefinitely, then collision safety is improved, but road efficiency and productivity deteriorate
Solution Approach 1:
The system performs preliminary identification of yield scenarios and determination of yielding obligations before actual yield execution. By analyzing sensor data, identifying contenders, determining yield scenarios, and establishing waiting periods in advance, the system resolves the contradiction by preparing the yielding action beforehand rather than reacting indefinitely to collision avoidance needs.
2Reliability
If conventional autonomous vehicles encode and deploy yielding protocols, then predictability and reliability improve, but system complexity increases
Solution Approach 1:
The yielding protocol system is segmented into distinct functional modules: sensor data acquisition, yield scenario identification, contender identification, yield determination logic, waiting period establishment, and trajectory generation. This segmentation makes the complex system manageable and deployable while maintaining high predictability and reliability through structured processing of yielding obligations.
3Reliability
If autonomous vehicles evaluate trajectories and conflicts in real-time, then safety and reliability improve, but computational time and processing duration increase
Solution Approach 1:
The system generates candidate trajectories and evaluates potential conflicts before actual navigation decisions are required. By preliminarily identifying yield scenarios, determining yielding obligations, and establishing waiting periods in advance, the system reduces real-time computational burden while maintaining high safety standards through pre-evaluated trajectory planning.
Data Source
AI summary
In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.