When Vehicle Crowd-Sensing Meets Complex Driving: Robust Illegal Roadside Parking Detections

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.