Security and Safety in Environmental Perception Systems with Applications to Autonomous Systems

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The proposed research program is aimed at increasing safety and security in environmental perception systems of applications in autonomous systems. Enhancing the environmental perception system used in applications such as autonomous vehicles (AVs) will help to shape the future of smart and safe autonomous and semi-autonomous transportation. Vehicle crashes cause over 50 million injuries and 1 million deaths per year worldwide, and are often attributable to human error. In addition, more than 20% of adults in developed countries are unable to drive, due to visual impairment and other disabilities. In light of this, AVs offer a new mobility medium in the automotive industry. Over the past decade, significant progress has been made in the development of autonomous driving systems. Commercial vehicles have offered technologies such as assisted and automated parking, adaptive cruise control, and even highway piloting, achieving a high level of autonomy. However, this necessitates secure and reliable environmental perception systems capable of operating in all-weather conditions (e.g., fog, rain, snow), and ready to deal with any cyber and adversarial attacks. To achieve this, multiple different sensors such as radar, camera, and LiDAR may be installed on the vehicles to sense the surrounding environment of a vehicle to improve reliability and safety. The reliability of AVs' driving decisions is dependent on sensory data which is a potential threat surface. Developing a framework to protect AV systems against these attacks is an indispensable component of any AV systems. We will develop and design sensor data integrity verification-based techniques to enhance the security of sensor data. Sensor data verification can be addressed using encryption or data hiding based techniques such as watermarking techniques. In addition, in the past few years, adopting artificial intelligence (AI) has transformed the capability of perception systems in AVs to a great extent. AI components are known to be highly vulnerable to a variety of attacks which could compromise proper functionality of autonomous systems. Adversarial attacks make small perturbations in the input which may not be detected by humans yet may lead to a misclassification with high confidence by AI models. In this research program, we intend to address the potential risks related to AI components of digital systems in AVs, including environment perception and path planning, in order to enhance the safety and security capabilities of autonomous perception systems. This will be achieved by employing multi-modal sensors in conjunction with advanced signal processing and AI techniques. This research will lead to impact on reliability and security of AVs systems. Over time, this work will influence widespread deployment of AVs across Canada.