Many scenarios in mobility and traffic involve multiple different agents that
need to cooperate to find a joint solution. Recent advances in behavioral
planning use Reinforcement Learning to find effective and performant behavior
strategies. However, as autonomous vehicles and vehicle-to-X communications
become more mature, solutions that only utilize single, independent agents
leave potential performance gains on the road. Multi-Agent Reinforcement
Learning (MARL) is a research field that aims to find optimal solutions for
multiple agents that interact with each other. This work aims to give an
overview of the field to researchers in autonomous mobility. We first explain
MARL and introduce important concepts. Then, we discuss the central paradigms
that underlie MARL algorithms, and give an overview of state-of-the-art methods
and ideas in each paradigm. With this background, we survey applications of
MARL in autonomous mobility scenarios and give an overview of existing
scenarios and implementations.

该论文介绍了多智能体强化学习在自主移动中的应用，包括行为规划、车辆之间通信、性能提升等方面，并探讨了该领域的一些最新方法和想法。

多智能体强化学习介绍及其在自主移动中的应用综述

An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility

Cooperative perception, or collective perception (CP) is an emerging and
promising technology for intelligent transportation systems (ITS). It enables
an ITS station (ITS-S) to share its local perception information with others by
means of vehicle-to-X (V2X) communication, thereby achieving improved
efficiency and safety in road transportation. In this paper, we present our
recent progress on the development of a connected and automated vehicle (CAV)
and intelligent roadside unit (IRSU). We present three different experiments to
demonstrate the use of CP service within intelligent infrastructure to improve
awareness of vulnerable road users (VRU) and thus safety for CAVs in various
traffic scenarios. We demonstrate in the experiments that a connected vehicle
(CV) can "see" a pedestrian around the corners. More importantly, we
demonstrate how CAVs can autonomously and safely interact with walking and
running pedestrians, relying only on the CP information from the IRSU through
vehicle-to-infrastructure (V2I) communication. This is one of the first
demonstrations of urban vehicle automation using only CP information. We also
address in the paper the handling of collective perception messages (CPMs)
received from the IRSU, and passing them through a pipeline of CP information
coordinate transformation with uncertainty, multiple road user tracking, and
eventually path planning/decision making within the CAV. The experimental
results were obtained with manually driven CV, fully autonomous CAV, and an
IRSU retrofitted with vision and laser sensors and a road user tracking system.

本文介绍了合作感知技术在智能交通系统中的应用，通过车路协同，加强对行人等容易忽视的交通参与者的感知以提升自动驾驶汽车的安全性。作者指出，合作感知技术是智能交通系统的未来发展方向之一。