Unified Cost Function for Joint Behavior and Trajectory Planning
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Solution Overview
Problem
Current autonomous vehicle motion planning systems lack an integrated approach that jointly optimizes behavioral planning and trajectory planning, leading to suboptimal decision-making in complex scenarios due to separate and uncoordinated planning stages.
Innovation Solution
A machine-learned motion planning system that includes a unified cost function shared by both behavioral planning and trajectory planning stages, allowing for joint training and optimization of behaviors and trajectories using a combined loss function, which considers safety, comfort, feasibility, and traffic rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate and uncoordinated planning stages are used for behavioral planning and trajectory planning, then the system complexity is reduced and ease of manufacture is improved, but the decision-making quality and navigation accuracy deteriorate
Solution Approach 1:
The patent merges behavioral planning and trajectory planning into a unified machine-learned motion planning system that jointly optimizes both aspects. The system uses a shared cost function and end-to-end training approach to coordinate behavior selection and trajectory generation, eliminating the suboptimal decision-making that arises from separate planning stages while maintaining system implementability through integrated architecture.
Solution Approach 2:
The unified motion planning system performs multiple functions simultaneously: it selects appropriate driving behaviors, generates corresponding trajectories, and optimizes both jointly through a single machine-learned model. This multi-functional approach allows the system to achieve coordinated optimization of behavior and trajectory without requiring separate specialized modules, thereby improving decision-making quality while keeping the system structure manageable.
2Manufacturing precision
If a unified cost function is used for joint optimization of behavioral planning and trajectory planning, then the motion planning accuracy and navigation efficiency are improved, but the device complexity and training difficulty increase
Solution Approach 1:
The patent combines behavioral planning and trajectory planning cost functions into a single unified cost function that evaluates both aspects simultaneously. This unified approach enables joint optimization through end-to-end training, improving motion planning accuracy by ensuring consistency between behavior selection and trajectory generation, while the integrated structure actually reduces overall system complexity compared to coordinating multiple separate functions.
Solution Approach 2:
The system uses parameter sharing across the machine-learned model to manage complexity. By sharing parameters and weights between the behavioral planning and trajectory planning components within the unified architecture, the system achieves joint optimization without proportionally increasing the number of independent parameters to train, thereby improving accuracy while keeping training complexity manageable through efficient parameter utilization.
3Loss of time
If separate training approaches are used for behavioral planning and trajectory planning, then the training time and computational resources are reduced, but the coordination and consistency between planning stages deteriorate
Solution Approach 1:
The patent implements end-to-end training that jointly trains behavioral planning and trajectory planning as a single integrated system. This approach uses a unified loss function that combines objectives from both planning stages, ensuring they are trained together to achieve proper coordination and consistency. The merged training approach eliminates the coordination failures that arise from separate training, and the efficient gradient propagation through the unified architecture actually reduces total training time compared to iterative separate training approaches.
Solution Approach 2:
The unified cost function provides feedback that simultaneously guides both behavioral planning and trajectory planning during training. The loss signal propagates through both components, allowing the system to learn coordinated patterns where behavior selections and trajectory generations are mutually optimized. This feedback mechanism ensures reliability and consistency between planning stages while the efficient unified computation reduces overall training time.
Data Source
AI summary
Systems and methods for generating motion plans for autonomous vehicles are provided. An autonomous vehicle can include a machine-learned motion planning system including one or more machine-learned models configured to generate target trajectories for the autonomous vehicle. The model(s) include a behavioral planning stage configured to receive situational data based at least in part on the one or more outputs of the set of sensors and to generate behavioral planning data based at least in part on the situational data and a unified cost function. The model(s) includes a trajectory planning stage configured to receive the behavioral planning data from the behavioral planning stage and to generate target trajectory data for the autonomous vehicle based at least in part on the behavioral planning data and the unified cost function.