  Multi-Agent Reinforcement Learning (MARL) has become a promising solution for
constructing a multi-agent autonomous driving system (MADS) in complex and
dense scenarios. But most methods consider agents acting selfishly, which leads
to conflict behaviors. Some existing works incorporate the concept of social
value orientation (SVO) to promote coordination, but they lack the knowledge of
other agents' SVOs, resulting in conservative maneuvers. In this paper, we aim
to tackle the mentioned problem by enabling the agents to understand other
agents' SVOs. To accomplish this, we propose a two-stage system framework.
Firstly, we train a policy by allowing the agents to share their ground truth
SVOs to establish a coordinated traffic flow. Secondly, we develop a
recognition network that estimates agents' SVOs and integrates it with the
policy trained in the first stage. Experiments demonstrate that our developed
method significantly improves the performance of the driving policy in MADS
compared to two state-of-the-art MARL algorithms.
