With the rapid advancement of artificial intelligence and communication technologies, connected autonomous vehicle (CAV) technology has made tremendous progress. However, due to the complexity of mixed traffic, CAV has not yet achieved scaled applications. Dedicated lanes for autonomous driving could provide a safer and more efficient driving environment for CAV, which would contribute to the realization of scaled applications for autonomous driving. Existing research extensively discusses the effectiveness of dedicated lanes, but most of these studies only consider the static spatial load attributes of roads, neglecting the importance of dynamic time scheduling optimization. To bridge this gap, this study firstly considers the interrelationship between ordinary lanes, traffic signals, and dedicated lanes comprehensively, and proposes a method for choosing deployment routes for autonomous driving dedicated lanes. Subsequently, based on the information provided by high definition maps (HDMaps), this research proposes a method for dynamically adjusting the usage rights of dedicated lanes for CAV. Finally, we conduct related comparative experiments to evaluate the impacts of the penetration rate of CAVs in mixed traffic, the occupancy rate in dedicated lanes, and the dynamic scheduling strategy on the overall efficiency of the traffic network.
Impact of HDMap-based Dynamic Dedicated Lanes for CAVs in Urban Networks
24.09.2023
2871382 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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