Autonomous Vehicle Motion Planning With RL Trajectory Evaluation
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Solution Overview
Problem
Existing motion planning techniques for autonomous vehicles, whether rule-based or machine learning-based, face challenges in scalability and adaptability in complex dynamic scenarios, often relying on human expert data and failing to adequately explore critical safety and performance aspects.
Innovation Solution
A machine learning-based approach using reinforcement learning (RL) for training a trajectory evaluator that learns from its experience without relying on human-generated data, enabling the selection of trajectories that optimize for safety and speed within speed limits by updating evaluation values based on performance and expected future performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based motion planning techniques are used, then explicit cost functions can be defined for trajectory evaluation, but the system becomes hard to maintain and does not scale well in complex dynamic scenarios
Solution Approach 1:
The patent replaces rule-based motion planning with a deep reinforcement learning model that directly maps sensor inputs to steering decisions. This substitution eliminates the need for manually defined cost functions and rule-based evaluation, making the system more adaptable to complex dynamic scenarios while maintaining trajectory evaluation accuracy through learned policies.
Solution Approach 2:
The reinforcement learning agent learns optimal trajectories autonomously through self-play and exploration in simulated environments, without requiring explicit human-defined cost functions or rules. The system improves its own performance through continuous training and experience accumulation, making it easier to maintain while handling complex dynamics.
2Adaptability or versatility
If machine learning-based approaches learning from human driving patterns are used, then trajectories similar to human driving can be identified, but the approach is limited to expert performance and fails to explore less critical safety and performance aspects
Solution Approach 1:
Instead of learning from human experts, the patent inverts the approach by having the system learn from itself through self-play and exploration. The reinforcement learning agent discovers novel driving behaviors and safety considerations that go beyond human expert patterns, exploring edge cases and critical scenarios that humans may not encounter or consider.
Solution Approach 2:
The system employs dynamic exploration through reinforcement learning, where the agent continuously adapts its behavior based on real-time feedback from the environment. This dynamic learning process enables the system to explore a broader range of trajectories including those that challenge safety and performance boundaries, rather than being constrained to static human-driven patterns.
3Adaptability or versatility
If reinforcement learning is used for training trajectory evaluator, then the system can learn from experience without human-generated data, but more computational resources and training time are required
Solution Approach 1:
The patent performs preliminary training in simulated environments before deployment, allowing the reinforcement learning agent to accumulate extensive experience and converge to optimal policies offline. This preliminary action in simulation reduces the training time required in real-world scenarios and enables the system to achieve independent learning capability without excessive computational resources during actual operation.
Data Source
AI summary
Methods and systems for training a motion planner for an autonomous vehicle are described. A trajectory evaluator agent of the motion planner receives state data defining a current state of the autonomous vehicle and an environment at a current time step. Based on the current state, a trajectory is selected. A reward is calculated based on performance of the selected trajectory in the current state. State data is received for a next state of the autonomous vehicle and the environment at a next time step. Parameters of the trajectory evaluator agent are updated based on the current state, selected trajectory, computed reward and next state. The parameters of the trajectory evaluator agent are updated to assign an evaluation value for the selected trajectory that reflects the calculated reward and expected performance of the selected trajectory in the future states.