Research summary

According to Gartner, the number of vehicles with autonomous driving capability will reach 2.5 million by 2028. While autonomous vehicles, especially cars, promise to deliver many benefits including travel comfort, improved safety and a reduction in the number of road accidents and deaths due to human error, there are concerns around adoption including trust, privacy, reliability, liability, crime, security and resilience. Autonomous cars are highly connected vehicles that use large sets of data from a wide range of external and internal sensors – such as lidar, radar, cameras and ultrasonic sensors. Artificial intelligence (AI), especially machine learning (ML) techniques including deep learning (DL) have a critical role in processing these data to train and validate automation tasks and make real-time decisions to navigate through traffic effectively and safely. Unfortunately, the new technology that enables autonomous cars, including the operating environment and supporting infrastructure is vulnerable to cyber-attacks. Moreover, we are yet to understand how criminals might monetise (or otherwise benefit from) attacks against autonomous vehicles or use them as a tool for crime.

Possible security threats include coordinated attacks using multiple autonomous vehicles, exploiting autonomous vehicles as weapons, attacking other vehicles such as police cars, using ransomware for extortion, blocking roads, tunnels and other critical infrastructure, diverting traffic, stealing sensitive personal data and property, helping criminals escape or watch potential robbery locations, and cause collisions by hiding objects on the road. Autonomous vehicles might also enable new crimes that we have not considered yet.

Consequently, this scoping study will bring together academics, researchers, industry, government, professionals and relevant professional bodies to consider the emerging and future crimes related to autonomous vehicle systems.  Participants will be asked to consider the likely success of these new types of crimes, crime surface scalability, skills set required, economic motivations and what should be done to prevent them. The study will help to identify emerging crimes, and shape (and prioritise) future research directions to improve the security of future technological developments and legislation.

Lead Investigator(s)

Dr Nilufer Tuptuk, UCL Security and Crime Science (n.tuptuk@ucl.ac.uk)

Outputs