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WU Y J , LI S B , CORTÉS J , et al. Distributed sliding mode control nonlinear heterogeneous platoon systems with positive definite topologies [J ] . IEEE Transactions on Control Systems Technology , 2020 , 28 ( 4 ): 1272 - 1283 . DOI: 10.1109/TCST.87 http://doi.org/10.1109/TCST.87 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=87 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=87

SEGATA M , CIGNO R L , DRESSIER F , et al. ACM MobiCom 2012 Poster: a simulation tool for automated platooning in mixed highway scenarios [J ] . Mobile Computing and Communications Review , 2012 , 16 ( 4 ): 46 - 49 . DOI: 10.1145/2436196.2436218 http://doi.org/10.1145/2436196.2436218 https://dl.acm.org/doi/10.1145/2436196.2436218 https://dl.acm.org/doi/10.1145/2436196.2436218 Automated platooning is one of the most challenging fields in the domain of Intelligent Transportation Systems (ITS). Conceptually, platooning means creating clusters of vehicles which closely follow each other autonomously without action of the driver, neither for accelerating, nor for braking. This leads to several important benefits from substantially improved road throughput to increased safety. The control of such platoons depends on two components: First, radar is typically to be used to control the distance between the vehicles, and secondly, Inter-Vehicle Communication (IVC) helps managing the entire platoon allowing cars to join or to leave the group whenever necessary. Platooning systems have been mostly investigated in controlled environments such as dedicated highways with centralized management. However, platooning-enabled cars will be deployed gradually and might have to travel on highways together with other non-automated vehicles. We developed a combined traffic and network simulator for studying strategies and protocols needed for managing platoons in such mixed scenarios. We show the models needed and present first results using a simple IVC-based platoon management as a proof of concept.

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GRIGORESCU , SORIN , TRASNEA , et al. A survey of deep learning techniques for autonomous driving [J ] . Journal of Field Robotics , 2020 , 37 ( 3 ): 362 - 386 . DOI: 10.1002/rob.21918 http://doi.org/10.1002/rob.21918 The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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