Core Concepts
AGENTSCODRIVER is a novel framework that leverages large language models to enable multiple vehicles to conduct collaborative driving with the capabilities of lifelong learning, reasoning, communication, and reflection.
Abstract
The paper proposes AGENTSCODRIVER, a large language model-powered framework for multi-vehicle collaborative driving with lifelong learning capabilities.
The key highlights are:
AGENTSCODRIVER consists of five modules: observation, reasoning engine, cognitive memory, reinforcement reflection, and communication. This architecture allows the agents to perceive the environment, reason about the situation, recall past experiences, reflect on their decisions, and communicate with other vehicles.
The reasoning engine utilizes large language models to synthesize information from the observation, memory, and communication to generate driving decisions. The cognitive memory module stores commonsense knowledge, past experiences, and reflections to enable lifelong learning.
The reinforcement reflection module evaluates the agent's decisions and provides detailed feedback to help the agent learn from its mistakes. The communication module allows agents to exchange information and negotiate with each other to realize collaborative driving.
Extensive experiments are conducted in highway and intersection scenarios, evaluating the framework's performance in both single-vehicle and multi-vehicle settings. The results demonstrate the effectiveness of AGENTSCODRIVER in achieving higher success rates and successful steps compared to baselines, especially with the increase in the number of memory items available to the agents.
The paper also discusses the framework's capabilities in comparison to other approaches, highlighting its advantages in areas such as cognitive memory, self-reflection, reinforcement reflection, collaborative driving, inter-vehicle communication, and lifelong learning.
Overall, AGENTSCODRIVER represents a significant advancement in autonomous driving by leveraging large language models to enable collaborative and lifelong learning capabilities.
Stats
The paper does not provide any specific numerical data or statistics to support the key logics. The evaluation is based on qualitative metrics such as success rate and successful steps.
Quotes
The paper does not contain any striking quotes that support the key logics.