21,201 research outputs found
Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles
Cooperative perception (CP) is a key technology to facilitate consistent and
accurate situational awareness for connected and autonomous vehicles (CAVs). To
tackle the network resource inefficiency issue in traditional broadcast-based
CP, unicast-based CP has been proposed to associate CAV pairs for cooperative
perception via vehicle-to-vehicle transmission. In this paper, we investigate
unicast-based CP among CAV pairs. With the consideration of dynamic perception
workloads and channel conditions due to vehicle mobility and dynamic radio
resource availability, we propose an adaptive cooperative perception scheme for
CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and
human-driven vehicles. We aim to determine when to switch between cooperative
perception and stand-alone perception for each CAV pair, and allocate
communication and computing resources to cooperative CAV pairs for maximizing
the computing efficiency gain under perception task delay requirements. A
model-assisted multi-agent reinforcement learning (MARL) solution is developed,
which integrates MARL for an adaptive CAV cooperation decision and an
optimization model for communication and computing resource allocation.
Simulation results demonstrate the effectiveness of the proposed scheme in
achieving high computing efficiency gain, as compared with benchmark schemes.Comment: Accepted by IEEE Transactions on Wireless Communication
Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication
This paper investigates a cooperative motion planning problem for large-scale
connected autonomous vehicles (CAVs) under limited communications, which
addresses the challenges of high communication and computing resource
requirements. Our proposed methodology incorporates a parallel optimization
algorithm with improved consensus ADMM considering a more realistic locally
connected topology network, and time complexity of O(N) is achieved by
exploiting the sparsity in the dual update process. To further enhance the
computational efficiency, we employ a lightweight evolution strategy for the
dynamic connectivity graph of CAVs, and each sub-problem split from the
consensus ADMM only requires managing a small group of CAVs. The proposed
method implemented with the receding horizon scheme is validated thoroughly,
and comparisons with existing numerical solvers and approaches demonstrate the
efficiency of our proposed algorithm. Also, simulations on large-scale
cooperative driving tasks involving 80 vehicles are performed in the
high-fidelity CARLA simulator, which highlights the remarkable computational
efficiency, scalability, and effectiveness of our proposed development.
Demonstration videos are available at
https://henryhcliu.github.io/icadmm_cmp_carla.Comment: 15 pages, 10 figure
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