The emerging technology of the Autonomous Truck Mounted Attenuator (ATMA), a
leader-follower style vehicle system, utilizes connected and automated vehicle
capabilities to enhance safety during transportation infrastructure maintenance
in work zones. However, the speed difference between ATMA vehicles and general
vehicles creates a moving bottleneck that reduces capacity and increases queue
length, resulting in additional delays. The different routes taken by ATMA
cause diverse patterns of time-varying capacity drops, which may affect the
user equilibrium traffic assignment and lead to different system costs. This
manuscript focuses on optimizing the routing for ATMA vehicles in a network to
minimize the system cost associated with the slow-moving operation.
To achieve this, a queuing-based traffic assignment approach is proposed to
identify the system cost caused by the ATMA system. A queuing-based
time-dependent (QBTD) travel time function, considering capacity drop, is
introduced and applied in the static user equilibrium traffic assignment
problem, with a result of adding dynamic characteristics. Subsequently, we
formulate the queuing-based traffic assignment problem and solve it using a
modified path-based algorithm. The methodology is validated using a small-size
and a large-size network and compared with two benchmark models to analyze the
benefit of capacity drop modeling and QBTD travel time function. Furthermore,
the approach is applied to quantify the impact of different routes on the
traffic system and identify an optimal route for ATMA vehicles performing
maintenance work. Finally, sensitivity analysis is conducted to explore how the
impact changes with variations in traffic demand and capacity reduction.

优化 Autonomous Truck Mounted Attenuator 车辆的路径选择，通过排队等待交通分配方法降低系统成本，考虑时间变化的容量下降造成的影响以及不同路径对交通系统的影响进行量化分析。

基于排队交通分配方法的智能工作区应用中的领导者 - 跟随者自动驾驶车辆系统部署

Deployment of Leader-Follower Automated Vehicle Systems for Smart Work  Zone Applications with a Queuing-based Traffic Assignment Approach

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。