Real-Time Density Detection in Connected Vehicles: Design and Implementation

摘要

Density information plays an important role in intelligent transportation systems for not only traffic control but also information sharing. Existing products have been able to provide coarsegrained density services. For example, Google Maps can illustrate the traffic conditions by different colors via Internet connection. Vehicle-to-vehicle wireless communications can locally acquire the density by information exchange and neighbor counting. However, either the Internet access or one-by-one counting leads to a sub-second-level delay, which cannot satisfy real-time vehicular applications such as autonomous navigation and data dissemination. To speed up density acquisition, we propose an RDD system. Leveraging the frequency resource, RDD divides the wireless channel into fine-grained subchannels and detects the neighbors in a parallel manner. We establish a testbed using software defined radios and experimentally validate RDD. Moreover, to evaluate RDD in high-density scenarios, extensive simulations are conducted based on real collected data. Both the experiment and simulation results demonstrate that RDD achieves 100 ms level density detection, while the state-of-the-art time-domain acceleration method is at the 10 ms level.