Illegal roadside parking is highly common in major cities worldwide, leading to traffic congestion & accidents and undermining human safety. Traditional methods for detecting illegal parking rely heavily on active human effort and customized sensors, which are extremely cost-ineffective to cover large-scale cities. To this end, we explore utilizing massive on-road vehicles to collect parking violation-related data (including surrounding videos and driving state information), enabling large-scale and low-cost illegal parking detection. However, the dynamic and unpredictable motion of the sensing and target vehicles, coupled with intricate driving behaviors and traffic conditions, pose great challenges to achieving accurate detection. To address these challenges, we propose$i$Patrol+, a novelrobustillegal parking detection system empowered by vehicle crowdsensing, at the heart of which liesa key extension of the Doppler effect from the traditional acoustic scenarios to the vehicle-mounted video scenarios in intricate driving conditions. Specifically, following the methodology of the Doppler effect and leveraging camera imaging theory, we establish a new vehicle speed estimation model via frame-to-frame feature shifts. Furthermore, this model is augmented by a feature re-projection scheme and a driving behavior-aware bias rectification scheme to effectively alleviate the negative effect of intricate driving conditions. We implement$i$Patrol+on smartphones mounted behind the vehicle windshields and conduct on-road experiments covering 233$\rm {km}$of roads in a 125$\rm {km}^{2}$urban area. The experimental results demonstrate that$i$Patrol+identifies illegal parking events with a precision of 86% in intricate driving conditions (e.g.,turns/u-turns, lane changes, traffic congestion, and waiting at traffic signals), outperforming five baselines by 17% on average.

Illegal roadside parking is a common problem faced by large-scale cities, leading to traffic congestion & accidents, and hindering fire rescue. Traditional methods for detecting illegal parking rely highly on active human efforts and particular sensors, which are extremely cost-ineffective to cover large-scale cities. To this end, we consider employing massive on-road vehicles to collect the vehicular sensory data (including recording video of the surroundings and driving state information), thereby enabling a large-scale, fine-grained illegal parking detection at a low cost. However, the dynamic and complex movement of the sensing and target vehicles, coupled with complex traffic situations and environmental factors, presents challenges for achieving accurate detection. To address these challenges, we propose iPatrol, an illegal roadside parking detection system leveraging on-road vehicle crowdsensing, at the heart of which lies a key extension of the Doppler effect from the traditional acoustic scenarios to the vehicle-mounted video scenarios. Following the methodology of the Doppler effect and leveraging camera imaging theory, we establish a new vehicle speed estimation model, using video feature’s change to estimate the relative speed of the two vehicles. Furthermore, this model is utilized to identify the parking status of the target vehicle and estimate its position by utilizing the graph rigidity theory and the non-convex optimization scheme. We implement iPatrol on Android smartphones mounted behind the vehicle windshields and conduct on-road experiments covering 233 km roads in an urban area about 125 km2. The experimental results demonstrate that iPatrol detected a total of 162 illegal parking events while achieving a detection accuracy of 87.1% which outperforms three baselines by 21.9% on average.