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This paper first discusses the advancement and challenges in the areas of developing Collision Avoidance Systems, or CAS. CAS have been on the market for a decade, and their development has been rapid. Starting with forward collision warning with brake support, targeting vehicles moving in the same direction in front of the car, CAS now cover pedestrians and cyclists in front of the car as well as vehicles standing still and even some situations of approaching vehicles in crossings. This development up to date is described and discussed according to the challenge areas of detection, decision strategy and intervention strategy. Next, the paper discusses assessment of system effects on driving safety. Numerous studies have tried to predict the effect of various CAS, and the real world effect of these systems has been shown to be significant.

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.

Frontal Severe Partial Overlap Collisions (SPOC) also called small overlap crashes pose special challenges with respect to structural design as well as occupant protection. In the early 1990s, the SPOC test method was developed addressing 20-40% overlap against a fixed rigid barrier with initial velocities up to 65 km/h. The knowledge gained has been used in the design of Volvo vehicles since then. Important design principles include front side members orientated along the wheel envelopes together with a strong support structure utilizing a space frame principle with beams loaded mainly in tension and compression. This novel setup was first introduced in the 850-model in 1991 and has been refined and patented (2001) in later Volvo front structures. Among the design principles are multiple front side members on each side, helping energy absorption efficiency and robustness.

The Inflatable Curtain (IC) has shown great potential to reduce head injuries in side impacts. This study explores and presents enhanced performance in two steps of improved activation algorithms. Crash data analysis, 21 full scale crash tests and component tests in a custom built drop tower rig have been performed. The IC performance in wider crash scenarios, including side impacts outside the occupant compartment (L-type impacts), was evaluated. Both statistical crash data and in-depth studies were used. It was found in the analysis of real life crashes that moderate to fatal head injuries can occur without intrusion in the occupant compartment. In L-type side impacts, the motion of the occupant relative to the vehicle interior may cause a head impact of sufficiently high severity to cause moderate to severe head injuries. A combined analysis of real world crash data and crash test results indicates that a substantial reduction in moderate to fatal head injuries can be achieved.

One way of avoiding crashes or mitigating the consequences of a crash is to apply an autonomous braking system. Quantifying the benefit of such a system in terms of injury reduction is a challenge. At the same time it is a fundamental input into the vehicle development process. This paper describes a method to estimate the effectiveness of reducing speed prior to impact. A holistic view of quantifying the benefit is presented, based on existing real life crash data and basic dynamic theories. It involves a systematic and new way of examining accident data in order to extract information concerning pre-crash situations. One problem area when implementing collision mitigation systems is being able to achieve sufficient target discrimination. The results from the case study highlight frontal impact situations from real world accident data that have the greatest potential in terms of improving accident outcome.