Cooperatively utilizing both ego-vehicle and infrastructure sensor data via
V2X communication has emerged as a promising approach for advanced autonomous
driving. However, current research mainly focuses on improving individual
modules, rather than taking end-to-end learning to optimize final planning
performance, resulting in underutilized data potential. In this paper, we
introduce UniV2X, a pioneering cooperative autonomous driving framework that
seamlessly integrates all key driving modules across diverse views into a
unified network. We propose a sparse-dense hybrid data transmission and fusion
mechanism for effective vehicle-infrastructure cooperation, offering three
advantages: 1) Effective for simultaneously enhancing agent perception, online
mapping, and occupancy prediction, ultimately improving planning performance.
2) Transmission-friendly for practical and limited communication conditions. 3)
Reliable data fusion with interpretability of this hybrid data. We implement
UniV2X, as well as reproducing several benchmark methods, on the challenging
DAIR-V2X, the real-world cooperative driving dataset. Experimental results
demonstrate the effectiveness of UniV2X in significantly enhancing planning
performance, as well as all intermediate output performance. Code is at
this https URL

利用 V2X 通信通过合作利用自车和基础设施传感器数据已成为对于先进自主驾驶的一种有希望的方法。本文介绍 UniV2X，一个开创性的合作自主驾驶框架，将所有关键驾驶模块无缝集成到一个统一的网络中，并提出了一种稀疏稠密混合数据传输和融合机制，以有效地实现车辆和基础设施的合作。实验结果表明 UniV2X 在显著提高规划性能和所有中间输出性能方面的有效性。

端到端自动驾驶通过 V2X 合作

End-to-End Autonomous Driving through V2X Cooperation

Anticipating possible future deployment of connected and automated vehicles
(CAVs), cooperative autonomous driving at intersections has been studied by
many works in control theory and intelligent transportation across decades.
Simultaneously, recent parallel works in robotics have devised efficient
algorithms for multi-agent path finding (MAPF), though often in environments
with simplified kinematics. In this work, we hybridize insights and algorithms
from MAPF with the structure and heuristics of optimizing the crossing order of
CAVs at signal-free intersections. We devise an optimal and complete algorithm,
Order-based Search with Kinematics Arrival Time Scheduling (OBS-KATS), which
significantly outperforms existing algorithms, fixed heuristics, and
prioritized planning with KATS. The performance is maintained under different
vehicle arrival rates, lane lengths, crossing speeds, and control horizon.
Through ablations and dissections, we offer insight on the contributing factors
to OBS-KATS's performance. Our work is directly applicable to many similarly
scaled traffic and multi-robot scenarios with directed lanes.

通过混合多智能体路径规划和无信号交叉口有序安排算法，本研究提出了一种性能显著优于现有算法、固定启发式以及基于 ARR 实施的优先规划的优化和完整算法 (OBS-KATS)，并在不同车辆到达率、车道长度、交叉速度和控制范围下保持性能，具有直接适用于类似规模的交通和多机器人场景的特点。

合作自主驾驶的多智能体路径规划

Multi-agent Path Finding for Cooperative Autonomous Driving

Cooperative autonomous driving plays a pivotal role in improving road
capacity and safety within intelligent transportation systems, particularly
through the deployment of autonomous vehicles on urban streets. By enabling
vehicle-to-vehicle communication, these systems expand the vehicles
environmental awareness, allowing them to detect hidden obstacles and thereby
enhancing safety and reducing crash rates compared to human drivers who rely
solely on visual perception. A key application of this technology is vehicle
platooning, where connected vehicles drive in a coordinated formation. This
paper introduces a vehicle platooning approach designed to enhance traffic flow
and safety. Developed using deep reinforcement learning in the Unity 3D game
engine, known for its advanced physics, this approach aims for a high-fidelity
physical simulation that closely mirrors real-world conditions. The proposed
platooning model focuses on scalability, decentralization, and fostering
positive cooperation through the introduced predecessor-follower "sharing and
caring" communication framework. The study demonstrates how these elements
collectively enhance autonomous driving performance and robustness, both for
individual vehicles and for the platoon as a whole, in an urban setting. This
results in improved road safety and reduced traffic congestion.

通过车辆之间的协作自主驾驶和车辆间通信，以车队行驶为应用场景，在城市环境中使用深度强化学习和物理模拟技术，实现了交通流量和安全的提升。