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Vehicle dynamics development relies on subjective assessments (SA), which is a resource-intensive procedure requiring both expert drivers and vehicles. Furthermore, development projects becoming shorter and more complex, and increasing demands on quality require higher efficiency. Most research in this area has focused on moving from physical to virtual testing. However, SA remains the central method. Less attention has been given to provide better tools for the SA process itself. One promising approach is to introduce computer-tablets to aid data collection, which has proven to be useful in medical studies. Simple software solutions can eliminate the need to transcribe data and generate more flexible and better maintainable questionnaires. Tablets’ technical features envision promising enhancements of SA, which also enable better correlations to objective metrics, a requirement to improve CAE evaluations.

ADAS (Advanced Driver Assistance System) functions can help the driver avoid accidents or mitigate their effect when they occur, and are pre-cursors to full autonomous driving (SAE defined as Level 4+). The main goal of this work is to develop a Model-Based system to actuate the Evasive Maneuver Assist (EMA) function. A typical scenario is the situation in which longitudinal Autonomous Emergency Braking (AEB) is too late and the driver has to adopt an evasive maneuver to avoid an object suddenly appearing on the road ahead. At this time, EMA can help improve the driver’s steering and braking operation in a coordinated way. The vehicle maneuverability and response performance will be enhanced when the driver is facing the collision. The function will additionally let the vehicle steer in a predetermined optimized trajectory based on a yaw rate set point and stabilize the vehicle. The EMA function is introduced with some analysis of benchmarking data.