TASCC: The Cooperative Car

可以说，我们正处于一个世纪以来最重大的汽车转型的开始，因为驾驶中涉及的复杂任务越来越多地由机器来完成。个人司机和他们的汽车将成为更广泛和更智能的城市交通基础设施的一部分，未来的汽车将是智能和合作的。提供更好的安全性、交通效率和更多生产力和愉快的旅程的机会是巨大的，但这种规模的革命面临着科学和社会的巨大挑战。现代汽车几乎潜移默化地配备了数百台微型计算机和传感器，包括相机、雷达、GPS和遥测，测量从速度、刹车、转向到环境条件的一切。许多车辆都有无线通信(从2018年起，新的欧盟汽车将拥有自动紧急呼叫的数据通信)，使数据能够实时上传到云中，供以后分析和使用。目前的车辆功能相对独立地运行，然而，这些数据为车辆提供了了解其驾驶员和环境的潜力，并为集成智能功能并最终实现自动驾驶汽车铺平了道路。尽管取得了重大进展，但仍有许多尚未解决的挑战，尤其是与此类汽车将如何被公众接受有关。到目前为止，自动驾驶汽车一直局限于较小的地理区域，例如谷歌的自动驾驶汽车依赖于事先由人和计算机分析准备的详细数据，无法完全应对恶劣天气、道路工程和其他现实世界中驾驶的方面。到目前为止，关于以下方面的研究还很少：自动驾驶汽车将如何适应当今的手动驾驶汽车；司机和乘员将如何与它们互动；它们将如何在我们的城镇与行人和骑自行车的人一起安全运行。加快向自动驾驶汽车的过渡，该项目将解决科学挑战，其解决方案将在主流汽车中提供未来自动驾驶汽车的一些便利、安全和效率优势，并将为完全自动驾驶汽车奠定基础。捷豹路虎的愿景是一款自学汽车(SLC)，它将最大限度地减少司机的分心，增强安全性，并提供个性化的驾驶体验。在这个项目中，我们将在机器学习和大数据流的处理和挖掘方面应用先进的研究技术，使SLC成为现实。例如，我们将使用遥测和乘员信息(如他们的认知负荷)来个性化驾驶体验，预测目的地，自适应地配置安全系统，就避免拥堵和停车机会提供建议。在不久的将来，车辆对车辆(V2V)和车辆对基础设施(V2X)的通信将成为现实：汽车将知道道路上其他汽车的情况，并能够与它们交换信息。汽车将变得相互合作：相互合作，与城市环境合作。当与现有传感器相结合时，车辆将能够共享道路、交通和停车情况的信息。该项目将开发软件算法，应用行为科学的实验方法，处理来自联网汽车的信息，以了解司机的习惯，并制定鼓励改变行为的策略：例如，设计自适应定价以减少停车和拥堵。我们还将调查人类司机和自动驾驶汽车如何进行最佳互动，例如在获得或移交控制权时，或者在与其他道路使用者互动和谈判时。为了提供安全高效的未来自动驾驶和半自动驾驶汽车，我们将开发智能驾驶员系统，以及学习驾驶员的合作和行为建模技术，使车辆能够相互合作，并与城市交通基础设施合作。

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.