Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis

• URL: http://arxiv.org/abs/2403.09571v1
• Date: Thu, 14 Mar 2024 17:00:29 GMT
• Title: Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis
• Authors: Fabio Maresca, Filippo Grazioli, Antonio Albanese, Vincenzo Sciancalepore, Gianpiero Negri, Xavier Costa-Perez,
• Abstract summary: We present a framework that monitors active vehicles using camera images and state information in order to determine whether vehicles are autonomous. Essentially, it builds on the cooperation among vehicles, which share their data acquired on the road feeding a machine learning model to identify autonomous cars. Experiments show it is possible to discriminate the two behaviors by analyzing video clips with an accuracy of 80%, which improves up to 93% when the target state information is available.
• Score: 6.422370188350147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The tremendous hype around autonomous driving is eagerly calling for emerging and novel technologies to support advanced mobility use cases. As car manufactures keep developing SAE level 3+ systems to improve the safety and comfort of passengers, traffic authorities need to establish new procedures to manage the transition from human-driven to fully-autonomous vehicles while providing a feedback-loop mechanism to fine-tune envisioned autonomous systems. Thus, a way to automatically profile autonomous vehicles and differentiate those from human-driven ones is a must. In this paper, we present a fully-fledged framework that monitors active vehicles using camera images and state information in order to determine whether vehicles are autonomous, without requiring any active notification from the vehicles themselves. Essentially, it builds on the cooperation among vehicles, which share their data acquired on the road feeding a machine learning model to identify autonomous cars. We extensively tested our solution and created the NexusStreet dataset, by means of the CARLA simulator, employing an autonomous driving control agent and a steering wheel maneuvered by licensed drivers. Experiments show it is possible to discriminate the two behaviors by analyzing video clips with an accuracy of 80%, which improves up to 93% when the target state information is available. Lastly, we deliberately degraded the state to observe how the framework performs under non-ideal data collection conditions.

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