Autonomous Path Planning With Latent State and Vehicle Interaction Graphs
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Solution Overview
Problem
Autonomous driving systems face challenges in identifying subtle cues for complex path planning in human environments, requiring improved methods to infer latent states and encode relationships between vehicles to enhance decision-making and safety.
Innovation Solution
A system combining deep reinforcement learning with graphical representation neural networks to infer latent states of surrounding vehicles, using an inference module to map sensor data to latent states and predict future trajectories, while generating a graphical representation to model influence passing between vehicles, thereby improving path planning and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning is used for path planning in complex driving scenarios, then the representational capability and generalization ability are improved, but the requirement for large amounts of data increases and unexpected behavior may occur on out-of-sample scenarios
Solution Approach 1:
The patent introduces latent states as an intermediary representation that captures the essential characteristics of other agents' behaviors. Instead of directly processing raw sensor data for decision-making, the system uses latent state inference to create a compressed, meaningful representation that generalizes better to unseen scenarios while reducing the data hunger of the reinforcement learning component.
Solution Approach 2:
The patent replaces the purely data-driven deep reinforcement learning approach with a hybrid system that incorporates latent state inference mechanisms. This substitution introduces a more efficient information processing pathway that doesn't rely solely on large-scale data collection and training, thereby reducing the data requirement while maintaining adaptability.
2Adaptability or versatility
If deep reinforcement learning is used for path planning, then the ability to model approximate solutions to intractable combinatoric relationships is improved, but the system remains prone to unexpected behavior on out-of-sample scenarios
Solution Approach 1:
The patent performs preliminary latent state inference before the reinforcement learning decision-making process. By pre-processing the sensor data to infer latent states that capture the essential behavior patterns of other agents, the system prepares a more robust representation that helps predict behavior in out-of-sample scenarios, thereby improving reliability before the actual path planning decision is made.
Solution Approach 2:
The latent states serve as an intermediary layer between raw sensor data and the reinforcement learning policy. This intermediary representation filters and structures information in a way that makes the system's behavior more predictable and reliable when encountering new scenarios, as the latent states capture fundamental behavior patterns that generalize across different situations.
3Ease of operation
If traditional rule-based or optimization-based approaches are used for autonomous driving, then the system is easier to interpret and control, but the scalability and generalization in complex driving scenarios are limited
Solution Approach 1:
The patent segments the autonomous driving system into distinct functional modules: latent state inference, reinforcement learning policy, and execution. The latent state inference component provides interpretability by explicitly modeling other agents' behaviors, while the reinforcement learning component handles complex decision-making. This segmentation allows each module to be optimized independently, maintaining interpretability where needed while achieving scalability in complex scenarios.
Solution Approach 2:
The patent transitions from traditional rule-based approaches operating in the physical space to a hybrid system that incorporates latent state space. By adding this abstract dimension for representing other agents' behaviors, the system achieves both interpretability (through explicit latent state modeling) and scalability (through the power of reinforcement learning in the latent space).
Data Source
AI summary
Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.