Cellular-vehicle-to-everything (C-V2X) technology has been increasingly adopted by the research community, automotive industry, and government agencies as the next key technology to enhance transportation safety and efficiency. Recent years have also witnessed emerging C-V2X-based applications, connecting sensors on vehicle (V2V), from infrastructure (V2I), and carried by pedestrians (V2P), to enable novel capabilities such as cooperative perception, sustainable transportation, and remote operations. While researchers have made successful strides in simulating these promising applications, the disconnect with real-world C-V2X performance often renders ungrounded assumptions, resulting in huge barriers towards deployment. In this poster, we conduct an application driven C-V2X network measurement using commercial off-the-shelf standard compliant C-V2X radios. Emulating the traffic patterns of popular C-V2X applications, we investigate the gap between the demand and reality.

Cellular-vehicle-to-everything (C-V2X) technology has been increasingly adopted by the research community, automotive industry, and government agencies as the next key technology to enhance transportation safety and efficiency. Recent years have also witnessed emerging C-V2X-based applications, connecting sensors on vehicles (V2V), from the infrastructure (V2I), and carried by pedestrians (V2P), to enable novel capabilities such as cooperative perception, sustainable transportation, and remote operations. While researchers have made successful strides in simulating these promising applications, the disconnect with real-world C-V2X performance often renders ungrounded assumptions, resulting in huge barriers towards deployment. In this paper, we aim to build and release a real-world C-V2X dataset, SEE-V2X, using commercial off-the-shelf standard compliant C-V2X radios. Emulating the traffic patterns of popular C-V2X applications, we investigate the gap between the demand and reality. Beyond throughput and latency, SEE-V2X contains cross-layer details, offers insight into the resource scheduling and allocation mechanism in various situations, and reveals the impact of nuanced configuration. Our preliminary analysis shows that severe packet collision and jitter can easily happen, indicating opportunities to avoid performance degradation with careful and subtle configuration. SEE-V2X dataset and the analysis tools are available at https://cisl.ucr.edu/SEE-V2X/.

Practitioners face substantial challenges in building multi-modal platforms that are essential for autonomous systems' safe decision-making. Those complications, including synchronization, calibration, and tedious sensor validation, hinder user adoption for real-world applications. We present CMS, a Cooperative Multi-modal Sensing Platform. CMS provides one consistent interface, integrating LiDAR, camera, RaDAR, and GNSS/IMU, streamlines these processes and makes the intricacies transparent to users and applications. Our demonstration shows that CMS can obtain high-quality multi-modal sensor data, paving the way toward real-world prototypes of cooperative autonomous systems.

Platooning has been proven to dramatically increase traffic flow and reduce fuel consumption, and vehicle-to-vehicle (V2V) communication and sensing are requisite for platooning stability. However, most of the existing works only address V2V communication or sensing functions respectively, which is far away from meeting the 6G requirements in availability and synchronization for platooning applications. Inspired by the recent advanced integrated sensing and communication (ISAC), in this paper, we propose VehicleTalk, a lightweight V2V communication and sensing framework. Essentially, VehicleTalk reuses the head/tail LED lights of the vehicles to construct communication/sensing channels for achieving concurrent message exchange and status awareness between adjacent vehicles. In particular, VehicleTalk innovates in both message transmission and vehicle sensing to improve communication robustness and lower system latency for platooning. It leverages Raptor Codes to combat serious packet loss in the V2V network. We also engineer a fast risk detection algorithm by simply monitoring the strength change of the received optical signals from the head/tail lights to improve safety in extreme cases such as emergency brake and cutting-in. Finally, we build a prototype of VehicleTalk with low-cost Commercial Off-The-Shelf (COTS) devices to quickly verify its effectiveness, and the extensive experimental results demonstrate the promising performance of VehicleTalk.