Vehicle Action Selection Using Simulated Trajectory Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles struggle to accurately perceive and navigate complex environments due to the challenges in trajectory planning, especially in rare or unseen scenarios, leading to inefficiencies and safety concerns.
Innovation Solution
A planning system that combines a learned planner with a weighted search mechanism, such as Monte Carlo tree search (MCTS), to generate and evaluate trajectory proposals, enhancing the vehicle's ability to adapt to dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a learned planner is used for trajectory planning, then the system can handle common scenarios efficiently, but it fails in rare or unseen scenarios
Solution Approach 1:
The system pre-generates multiple trajectory proposals using the learned planner before execution, evaluating each proposal through simulation and scoring. This preliminary action ensures that when rare or unseen scenarios occur, the system has already prepared multiple potential trajectories to choose from, improving adaptability without sacrificing efficiency in common scenarios
Solution Approach 2:
The system implements a feedback mechanism where trajectory proposals are evaluated through simulation environments, scored based on safety and feasibility criteria, and used to iteratively improve the planning decision. This feedback loop allows the system to learn from simulated outcomes and adjust trajectory selection, enabling better handling of rare scenarios while maintaining efficiency for common cases
2Reliability
If multiple trajectory proposals are generated and evaluated, then navigation safety improves, but computational complexity increases
Solution Approach 1:
The system generates multiple trajectory proposals (excessive action) but only evaluates and scores a subset of them through simulation, rather than exhaustively analyzing all possible trajectories. This partial evaluation approach maintains navigation safety by considering multiple options while controlling computational complexity by limiting the depth of analysis for each proposal
Solution Approach 2:
The system uses simulation environments as virtual copies of the real world to evaluate trajectory proposals. Instead of testing trajectories in the actual physical environment (which would be dangerous and time-consuming), the system creates and evaluates copies in simulated scenarios, improving safety while managing computational resources efficiently
Data Source
AI summary
A scene simulation system can use scene data of a scene of a vehicle to generate one or more simulated states and one or more simulated trajectories associated with the one or more simulated states. The system can evaluate the simulated trajectories and select an action for the vehicle based on the evaluation of the simulated trajectories.