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. Benchmarking has shown different strategies from several OEMs (Original Equipment Manufacturers) and suppliers, in which the discipline of how they want the vehicle performance to behave and where improvements could be found. 

Functional requirement and performance metrics of EMA function are created. The performance metrics are based on objective and subjective evaluation. 

Frequency analysis is used to develop transfer functions to understand actuator effects, and Model-Based Design (MBD) is employed to subsequently develop the controller function using co-simulation between Matlab/Simulink and VI-CarRealtime. Results and analysis are presented at key vehicle speeds where intervention could be necessary. 

The final part introduces driving simulator tests after the development of the function. The measured signal results could be compared with simulation results and driver’s performance without EMA. Eventually, a questionnaire survey was made to define a direction for further improvement. Additional ideas are presented for more advanced work.