Radar Detection in Vehicular Networks: Fine-Grained Analysis and Optimal Channel Access

Automotive radar is a critical feature in advanced driver-assistance systems. It is important in enhancing vehicle safety by detecting the presence of other vehicles in the vicinity. The performance of radar detection is, however, affected by the interference from radars of other vehicles as well as the variation in the target radar cross-section (RCS) due to varying physical features of the target vehicle. Considering such interference and random RCS, this work provides a fine-grained performance analysis of radar detection. Specifically, using stochastic geometry, we calculate the meta distribution of the signal-to-interference-and-noise ratio that permits the reliability analysis of radar detection at individual vehicles. We also evaluate the delay aspect of radar detection, namely, the mean local delay which is the average number of transmission attempts needed until the first successful target detection. For a given target distance, we obtain the optimal transmit probability that maximizes the density of successful detection while keeping the mean local delay below a threshold. We also provide several system design insights in terms of the fraction of reliable radar links, transmission delay, the density of vehicles, and congestion control. Finally, leveraging the derived framework, we present an online algorithm to select the transmit probability in a distributed manner, particularly when system parameters such as vehicular density and interferer locations are unknown to individual vehicles. We demonstrate the efficacy of the proposed online framework for selecting the optimal transmit probability within a deadline of 0.1 ms, which is critical for sustaining automotive safety applications and services.