Journal ArticleDOI
Research Advances and Challenges of Autonomous and Connected Ground Vehicles
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TLDR
A representative architecture of CAVs is introduced and the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAV are surveyed and their significant research issues enumerated.Abstract:
Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.read more
Citations
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Review of "L1 Adaptive Control Theory: Guaranteed Robustness with Fast Adaptation"
Journal ArticleDOI
Robust Platoon Control in Mixed Traffic Flow Based on Tube Model Predictive Control
TL;DR: In this paper, a robust platoon control framework is proposed based on tube MPC, where the prediction uncertainty is dynamically mitigated by the feedback control and restricted inside a set with a high probability.
Journal ArticleDOI
Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges
TL;DR: The representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle and the remaining challenges and unsolved problems are provided.
Proceedings ArticleDOI
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
TL;DR: In this paper, a federated learning based dynamic map fusion framework is proposed to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning.
Journal ArticleDOI
Decentralized federated learning for extended sensing in 6G connected vehicles
TL;DR: A new modular, decentralized approach to FL, referred to as consensus-driven FL (C-FL), suitable for PointNet compliant deep ML architectures and Lidar point cloud processing for road actor classification is proposed.
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