The increasing number of vehicles has led to issues such as traffic congestion, air pollution, and energy consumption. Intelligent connected autonomous driving (AD) offers a solution to these transportation challenges, but it requires cooperation among dedicated AD road networks, connected cloud-based control and dispatch systems, and intelligent vehicle sensing. To harness the potential of AD, it is crucial to consider the requirements of autonomous vehicles (AVs) when planning road networks. Well-designed road networks can enhance travel experiences, reduce energy consumption, and promote urban sustainability. In order to meet the needs of AD while ensuring the operational efficiency of urban transportation networks, this paper introduces a road network evaluation framework that encompasses both static and dynamic assessment criteria, following a detailed exploration and analysis of various road network generation methods. This evaluation framework addresses the deficiency in existing road network generation methods, which lack dynamic assessment of traffic networks. To validate the proposed approach, we have chosen a road network generation method based on conditional generative adversarial network (CGAN) as an application example. Through carefully designed experiments, we confirm the feasibility and effectiveness of the proposed method for generating and evaluating road networks tailored for AD scenarios.
Autonomous driving road network generation and evaluation based on generative artificial intelligence
International Conference on Smart Transportation and City Engineering (STCE 2023) ; 2023 ; Chongqing, China
Proc. SPIE ; 13018 ; 130183I
14.02.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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