Autonomous vehicles now share roads with human drivers, creating unpredictable mixed traffic that regulators struggle to monitor. Since AVs look identical to standard cars, authorities cannot manually track their behavior, leaving traffic planners without the data needed to manage flow, boost capacity, and protect public safety. Without reliable insight into AV behavior, infrastructure strategies and safety evaluations remain critically underinformed.
This system uses standard sensors like cameras and GPS to capture vehicle motion data, then applies machine learning to analyze subtle driving patterns such as speed, spacing, and trajectory. Within just 0.2 to 5 seconds, it classifies vehicles as autonomous or human driven and can even identify the manufacturer. Unlike simulation based or small-scale field approaches, this method works directly in real world mixed traffic, delivering near real time results without specialized infrastructure, making it a scalable and practical tool for traffic management.
(a)
(b)
Mixed traffic experiment locations. (a) Low-speed experiment. (b) High-speed experiment