Hardware-in-the-Loop Demo of a Cooperative Intersection where Cars don't Stop
Lorenzo Farina, Matteo Piccoli, Salvatore Iandolo, Antonio Solida, Carlo Augusto Grazia, Marco Rapelli, Francesco Raviglione, Claudio Casetti, Alessandro Bazzi
Abstract
The road transportation sector is undergoing a radical transformation, working for a future where all vehicles will be autonomous and connected. A key challenge in this vision concerns intersections, which will no longer rely on traffic lights or traditional right-of-way rules. Instead, algorithms and communication protocols will be employed to optimize traffic flow. In this context, we are developing a testbed where cars equipped with wireless connectivity exchange information with a remote controller and coordinate their maneuvers. As the vehicles navigate through different types of intersections with various routes, they communicate with the controller, adjusting their speed to pass through intersections without stopping. This demo specifically showcases a hardware-in-the-loop (HiL) implementation of such intelligent intersection, using the SUMO traffic simulator to emulate vehicle positions. Real hardware and software components, which are designed for use in vehicles and roadside infrastructure, are employed to enable real-time data exchange of contextual and control information, running the cooperative coordination algorithm.
Create a lesson
Related papers
A Hybrid and Hierarchical Ray Tracing Method for Efficient 3D Gaussian Rendering
Tiancheng Zhao, Sixu Li, Yuzhou Chen et al.
Anechoic: Onsager-Corrected Parallel Annealing for Sub-0.1 ms Dense Max-Cut on an FPGA
Seungki Hong, Kyeongwon Jeong, Taekwang Jang
TPCS: A Cycle-Level Simulation Paradigm with Native Execution Semantics for RTL Modeling and Generation
Chengzhen Meng, Xiaoyu Li, Chen Bai et al.
CacheFlex: Direct Software-Managed Access to Higher-Level Cache for Scalable Vector Support
Jingqun Zhang, Maohua Nie, Jiayi Wang et al.
VortexSynth: Profile-guided benchmark reconstruction for GPU architecture research
Huanzhi Pu, Chulhyung Park, Chihyo Ahn et al.
DEX: Digit-Level Early Exit for Energy-Efficient MSDF Neural Network Inference
Yousef Sadegheih, Dorit Merhof, Muhammad Usman