Hazard Situation Prediction Using Spatially and Temporally Distributed Vehicle Sensor Information

Driver assistance systems are the key technology to improve traffic safety and lower the number of deadly accidents. Direct communication between cars will further enhance this field of driver safety. In the context of foresighted driving, Bayesian networks can be used to determine a traffic situation at the current position of a car. Communicating this awareness for the current time and position will help other traffic participants. However, situations change dynamically and cars cannot trust all the information provided by other cars over time. Reasoning with this information is difficult as Bayesian networks cannot use spatial and temporal data in an appropriate way. This article outlines the spatial and temporal problems in predictive driver assistance and demonstrates how they can be solved by considering spatial and temporal influences by applying weighting techniques. The pre-processed information is utilized by a Bayesian network for further refinement. Thus, the proposed approach enables the detection and correct prediction of traffic situations. The approach is evaluated by predicting hazardous rain fields in a car by means of information received from other cars