Need of Ambient Intelligence for Next-Generation Connected and Autonomous Vehicles

The automotive industry is shifting its focus from performance and features to safety, entertainment, and driver comfort. In this regard, driver assistance and autonomous driving technology are gaining more attention. Such technology has the potential to reduce road accidents, traffic congestion, and fuel usage. However, vehicles cannot become fully autonomous, until they are able to sense their context efficiently (context sensing), and to use ambient learning to respond appropriately and within short timescales to the data they have sensed. Context sharing will also become essential, because a single vehicle will not be able to gain a holistic view of its context without cooperation from other nearby vehicles and from the roadside infrastructure. Indeed, there are further advantages when a group of vehicles make intelligent decisions based on a common understanding of their context. This chapter highlights the significance of ambient intelligence for next-generation connected and autonomous vehicles, describes its current state of the art, and also shows how its potential might be achieved. One of the main challenges refers to how to provision and coordinate cloud-based services to meet the needs of real-time (low latency) data-intensive (high data rate) ambient intelligence, particularly for safety-critical vehicular safety applications. It indicates how autonomous or semi-autonomous vehicles are likely to make seamless use of any available wireless networking technologies to improve both coverage and reliability and, where feasible, to cache critical content near the network edge so as to minimize the number of network hops and hence service latencies. Both of these approaches should improve the network quality of service afforded to driving applications.