Future of in-vehicle recommendation systems @ Bosch

Future in-vehicle recommendation systems will assist the driver or passenger in all situations before, along, and after a trip. Based on preferences and needs of the user and by taking the current situation and available context information into account, they will provide the right recommendation at the right time. Bosch is the world's largest automotive supplier, delivering a full range of products and services from power-train, infotainment, HMI, connected mobility, driver assistance to automated driving. This talk will present challenges, concepts and recent technical progress in in-vehicle recommendation systems developed at Bosch including details of a combined routing, charging, and point-of-interest (POI) recommendation system. There has been tremendous progress in the field of location-independent recommendation systems, such as recommending films, music, news or shopping articles. The ubiquity of user location information, provided by connected devices, has paved the way for location-based services (LBS), and their combination with social networks have extended these to location-based social network (LBSN) services, see [1, 6] for recent surveys about recommender systems in LBSN. In-vehicle recommendation systems go a step further by extending LBSN services with vehicle context and vehicle specific applications. This can support the user in various applications, such as routing (e.g. route and point of interest recommendation), infotainment (e.g. music or news recommendation), communication (finding a contact, fast call) and in-vehicle control (e.g. seat position, ambient light or HVAC settings). Out-of-vehicle assistance includes the control of connected devices in smart buildings such as alarm systems, heating, kitchen and entertainment devices. We present an important application of in-vehicle recommending systems, a combined routing, charging and POI recommender developed at Bosch. Routing and charging optimization for electric vehicles was described for optimizing the shortest feasible path [2], optimizing constrained shortest path [4], optimizing charging grid demand and opportunities [5], and optimizing minimum cost [3]. These approaches focus on single criteria based optimization. We describe the first system with combined route optimization, charging station search and POI recommendation. It optimizes three criteria: finding the optimal route with the optimal charging stations, so that the vehicle always has enough energy, and finding the optimal POIs along the route, where 'optimal' depends on the drivers preferences and rich context information covering user, vehicle and environment.