How can we make self-driving cars socially acceptable?
Towards Developing Socially Compliant Automated Vehicles: State of the Art, Experts Expectations, and A Conceptual Framework
January 13, 2025
https://arxiv.org/pdf/2501.06089This paper explores creating "socially compliant" autonomous vehicles (SCAVs) that can interact smoothly and safely with human drivers. It proposes a conceptual framework encompassing perception, decision-making (incorporating social cues and driving styles), planning, control, and a bidirectional behavioral adaptation module to enable mutual learning between human drivers and AVs.
Key points for LLM-based multi-agent systems:
- Implicit Communication Interpretation: SCAVs need to understand subtle human cues (e.g., waving, deceleration) like LLMs interpreting natural language nuances.
- Behavioral Adaptation: The proposed bidirectional adaptation module mirrors the need for agents in LLM-based systems to dynamically adapt to each other and human users.
- Multi-Objective Optimization: Balancing safety, efficiency, and comfort resonates with optimizing various factors in LLM-agent interactions, like response quality, speed, and user satisfaction.
- Multi-Agent Modeling: The paper's emphasis on interactions between AVs and human drivers mirrors the complexity of LLM-based multi-agent systems, where agents may need to cooperate or compete.
- Sensing and Perception: The focus on robust perception for AVs translates to the need for LLMs to accurately process complex information and extract relevant features for decision-making.
- Social and Cultural Alignment: The paper highlights the challenge of adapting to cultural driving norms, which parallels the need for LLMs and agents to be sensitive to diverse cultural contexts in communication.
- eHMI Communication: The use of external human-machine interfaces (eHMI) in SCAVs suggests the potential for designing explicit communication channels between agents and humans in LLM-based systems, going beyond natural language.