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Run off road events are frequent and can result in severe consequences. The reasons for leaving the road are numerous and the sequence the car is exerted to differs in most events. The objective of this study is to identify different situations and mechanisms both in respect to accident avoidance and occupant protection and to present test methods addressing the different identified mechanisms of run off road occupant safety. Mechanisms and influencing factors are identified using statistical and in-depth crash data as well as driving data. There are a number of reasons for leaving the road; driver fatigue, driver distraction and inadequate speed in relation to the traffic situation to mention a few. An outline of principle test methods for evaluating technology assisting the driver to stay on the road is presented in relation to the identified situations and mechanisms. Crash test methods for some typical run off road scenarios are suggested.

Different types of driver workload are suggested to impact driving performance. Operating a vehicle in a situation where the driver feel uneasy is one example of driver workload. In this study, passenger car driving data collected with Naturalistic Driving Study (NDS) data acquisition equipment was analyzed, aiming to identify situations corresponding to a high driver's subjective rating of ‘unease’. Data from an experimental study with subjects driving a passenger car in normal traffic was used. Situations were rated by the subjects according to experienced ‘unease’, and the Controller Area Network (CAN) data from the vehicle was used to describe the driving conditions and identify driving patterns corresponding to the situations rated as ‘uneasy’. These driving patterns were matched with the data in a NDS database and the method was validated using video data. Two data mining approaches were applied.