Path Planning With Latent State Inference for Multi-Agent Driving
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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 supervised learning and graphical representation neural networks to infer latent states of surrounding vehicles, enabling better path planning by modeling spatial-temporal relationships and influence passing between agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep reinforcement learning is used for path planning, then the representational capability and generalization improve, but the data requirement increases and unexpected behavior on out-of-sample scenarios occurs
Solution Approach 1:
The patent introduces latent states as an intermediary representation that captures the true nature of other agents (cooperative vs. aggressive). This latent state inference acts as a mediator between raw sensor data and path planning decisions, allowing the system to generalize better with less data by inferring the underlying intentions rather than relying solely on large amounts of training data.
Solution Approach 2:
The patent transforms the problem by changing the parameter representation from raw sensor data to inferred latent states. By mapping sensor observations to latent state distributions that represent agent intentions, the system achieves better generalization and reduces the need for extensive training data while improving representational capability.
2Measurement precision
If latent state inference is implemented, then the accuracy of identifying agent intentions improves, but the computational complexity increases
Solution Approach 1:
The patent segments the complex task of path planning into distinct modules: sensor data processing, latent state inference, trajectory prediction, and motion planning. The latent state inference is further segmented into inferring individual agent states and then combining them for collective behavior prediction. This segmentation reduces computational complexity by breaking down the overall complexity into manageable parts.
Solution Approach 2:
The patent performs preliminary action by inferring latent states of other agents before making path planning decisions. By pre-inferring the intentions and states of surrounding agents, the system prepares the necessary information in advance, reducing the computational burden during real-time decision-making and improving the accuracy of identifying agent intentions.
3Adaptability or versatility
If graphical representation with spatial-temporal relationships is used, then the modeling of agent interactions improves, but the system complexity increases
Solution Approach 1:
The patent applies a universal graphical representation framework that can model various types of agent interactions (cooperative, aggressive, neutral) using the same spatial-temporal relationship structure. This multi-functional approach allows the system to handle different interaction scenarios without requiring separate models, improving adaptability while managing system complexity through a unified framework.
Data Source
AI summary
Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. 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 also maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents of the plurality of 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.