TASCC: The Cooperative Car

We are at the start of, arguably, the most significant transition in motoring for a century as the complex tasks involved in driving become increasingly performed by machine. Individual drivers and their cars will form part of wider and smarter urban transport infrastructure, and the cars of the future will be intelligent and cooperative. The opportunities to deliver better safety, traffic efficiency, and more productive and pleasant journeys are enormous, but a revolution on this scale faces great challenges for science and society. Almost imperceptibly to the driver, modern vehicles are equipped with hundreds of micro-computers and sensors, including cameras, radar, GPS, and telemetry measuring everything from speed, braking, and steering to environmental conditions. Many vehicles have wireless communications (from 2018, new EU cars will have data communication for automated emergency calls) enabling data to be uploaded in real-time to the cloud to be later analysed and used. Current vehicle features operate relatively independently, however such data gives the potential for a vehicle to learn about its driver and environment, and paves the way for integrated intelligent features and eventually for autonomous cars. Despite significant progress, there are many unsolved challenges, not least related to how such cars will be accepted by the public. So far, autonomous vehicles have been confined to small geographic areas, for example Google's Self-Driving Car relies on detailed data prepared beforehand by human and computer analysis, and is unable to fully cope with adverse weather, road works and other real-world aspects of driving. There has been thus far little research on: how autonomous vehicles will fit in with today's manually driven cars; how drivers and occupants will interact with them; and how they will run safely in our towns, with pedestrians and cyclists. Accelerating the transition to autonomous vehicles, this project will tackle scientific challenges whose solutions will deliver some of the convenience, safety and efficiency benefits of future autonomous cars in mainstream vehicles, and will lay the foundation for fully autonomous vehicles. Jaguar Land Rover has a vision of a self-learning car (SLC) that will minimise driver distractions, enhance safety, and deliver a personalised driving experience. In this project, we will apply advanced research techniques in machine learning and the processing and mining of large data streams to make the SLC a reality. For example, we will use telemetry and information about the occupants, such as their cognitive load, to personalise the driving experience, predict the destination, adaptively configure safety systems, advise on congestion avoidance and parking opportunities. In the near future vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) communication will be a reality: cars will know about other cars on the road and be able to exchange information with them. Cars will become cooperative: with each other and with urban environments. When combined with existing sensors, vehicles will be able to share information on road, traffic and parking conditions. This project will develop software algorithms, applying experimental methods from behavioural sciences and processing information from connected cars to understand driver habits, and develop strategies to encourage behaviour modifications: for example to design adaptive pricing to reduce parking and congestion. We will also investigate how best human drivers and autonomous cars can interact, for example when taking or handing-over control, or when interacting and negotiating with other road users. In order to deliver safe and efficient autonomous and semi-autonomous cars of the future, we will develop intelligent driver systems, and cooperation and behaviour modelling techniques that learn about drivers, enabling vehicles to cooperate with each other and with urban transport infrastructures.