Securing the Perception of Advanced Driving Assistance Systems Against Digital Epileptic Seizures Resulting from Emergency Vehicle Lighting
The safety of autonomous cars has come under scrutiny in recent years, especially after 16 documented incidents involving Teslas (with autopilot engaged) crashing into parked emergency vehicles (police cars, ambulances, and firetrucks). Despite widespread public interest, the technical factors behind these accidents have remained largely unexplored. In this research, we unveil EpileptiCar , a digital epileptic seizure phenomenon that causes an object detector's confidence score to fluctuate when exposed to an activated emergency vehicle flasher. This vulnerability poses a significant risk, because not only does it cause autonomous vehicles to crash near emergency vehicles, but it can also be exploited by adversaries to cause such accidents. We delve into the underlying causes of the EpileptiCar phenomenon, assessing five commercial advanced driving assistance systems (HP, Pelsee, AZDOME, Imagebon, Rexing), four object detectors (YOLO, SSD, RetinaNet, Faster R-CNN), and 14 patterns of emergency lights to understand the influence of different factors. To mitigate this risk, we propose Caracetamol , a robust framework designed to enhance the resilience of object detectors against the effects of activated emergency vehicle flashers. Our evaluation shows that the implementation of Caracetamol improves the detection capabilities of three object detectors (YOLO, Faster R-CNN, and SSD) by 0.21 and reduces the confidence fluctuation range by 0.19, significantly enhancing safety in the presence of emergency lights.
Insight 1 : T he activation of emergency vehicle lighting creates a phenomenon, which we term the EpileptiCar phenomenon, that causes the confidence score of object detectors regarding a detected object (in our case it is a car) to fluctuate, within a wide score range, with the score dipping below a reasonable detection threshold in some cases.
