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State features under KinematicObservation setting.

State features under KinematicObservation setting.

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Figure 1: Cumulative reward on LANE-3-DENSITY-2 with progressively...
Figure 8: Architecture of (a) Policy networks (transformer encoders) π...
Figure 9: State features under KinematicObservation setting.
Figure 10: Prefix Prompt before interacting with HighwayEnv.
+3 Figure 11: Interaction between the LLM-based agent and the HighwayEnv...
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
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  • Oct 2024
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of purely data-driven methods. Current LLM-based agents require lengthy inference times and face challenges in interacting with real-time autonomous driving environments. A key...
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... The KinematicsObservation provides essential information about the neighbouring vehicles, such as their presence, positions in the x-y coordinate system, and velocities along the x and y axes. These features are represented as absolute values, independent of the agent's frame of reference, and are not subjected to any normalization process. Fig. 9 demonstrates the state features under KinematicObservation setting, in which we introduce the vehicle feature tokenization in Sec. 3.3. ...