@article{1157,
  keywords = {traffic control, intersection management, Autonomous vehicle},
  author = {Jian Kang and Dan Lin},
  title = {Highly Efficient Traffic Planning for Autonomous Vehicles to Cross Intersections Without a Stop},
  abstract = {Waiting in a long queue at traffic lights not only wastes valuable time but also pollutes the environment. With the advances in autonomous vehicles and 5G networks, the previous jamming scenarios at intersections may be turned into non-stop weaving traffic flows. Toward this vision, we propose a highly efficient traffic planning system, namely DASHX, which enables connected autonomous vehicles to cross multi-way intersections without a stop. Specifically, DASHX has a comprehensive model to represent intersections and vehicle status. It can constantly process large volumes of vehicle information, resolve scheduling conflicts, and generate optimal travel plans for all vehicles coming toward the intersection in real time. Unlike existing works that are limited to certain types of intersections and lack considerations of practicability, DASHX is universal for any type of 3D intersection and yields the near-maximum throughput while still ensuring riding comfort. To better evaluate the effectiveness of traffic scheduling systems in real-world scenarios, we developed a sophisticated open source 3D traffic simulation platform (DASHX-SIM) that can handle complicated 3D road layouts and simulate vehicles’ networking and decision-making processes. We have conducted extensive experiments, and the experimental results demonstrate the practicality, effectiveness, and efficiency of the DASHX system and the simulator.},
  year = {2023},
  journal = {ACM Trans. Intell. Syst. Technol.},
  volume = {14},
  number = {2},
  month = {mar},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  issn = {2157-6904},
  url = {https://doi.org/10.1145/3572034},
  doi = {10.1145/3572034},
}