About

Introduction

Machine learning (ML) applications are driving innovation in connected and autonomous vehicles (CAVs). To enable ”self-awareness,” CAVs are fitted with a range of perception sensors (lidar, radar, ultrasonic, camera), control sensors, communication systems (cellular, wifi, Bluetooth), control systems (velocity, steering, and braking), and ML-trained semi- and full-autonomy services. Controller area network (CAN) buses share critical sensory information with the onboard intelligent processing units, or other networks, forming the backbone of self-driving car perception. These components require a continuous assessment of potential threats to safety to allow the CAVs to navigate the road. ML models used for perception, anomaly detection, and emergency maneuvers in CAVs, rely heavily upon sensor data. Research has demonstrated how these models are vulnerable to spoofing and adversarial examples resulting from small-magnitude perturbations in the input data. Therefore, understanding inputs (e.g., sensor data) is a critical step toward creating resilient infrastructure within which smart agents like CAVs and human actors can co-exist, thereby reducing risks to life and property.

MSimulate the environment of autonomous vehicles using comma.ai simulation environment. During simulation, we capture the sensor data and use it to map the trajectory of the vehicle / Building explainable models to explain the behavior of attack and defense models.

Future Directions

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Project Phases

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