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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.

As vehicles become electrified and more intelligent in terms of sensing, actuation and processing; a number of interesting possibilities arise in controlling vehicle dynamics and driving behavior. Over-actuation with in- wheel motors, all wheel steering and active camber is one such possibility, which facilitate the control strategies that push boundaries in energy consumption and safety. Optimal control can be used to investigate the best combinations of control inputs to an over-actuated system. This paper shows how an optimal control problem can be formulated and solved for an over-actuated vehicle case, and highlights the translation of this optimal solution to a real-world scenario, enabling intelligent means to improve vehicle efficiency. This paper gives an insight into Dynamic Programming (DP) as an offline optimal control method that guarantees the global optimum.

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.

The automotive industry strives to develop high quality vehicles in a short period of time that satisfy the consumer needs and stand out in the competition. Full exploitation of simulation and Computer-Aided Engineering (CAE) tools can enable quick evaluation of different vehicle concepts and setups without the need of building physical prototypes. Addressing the aforementioned statements this paper presents a method for optimising the Electric Power-Assisted Steering (EPAS) ECU parameters employing solely CAE. The objective of the optimisation is to achieve a desired steering response. The developed process is tested on three specific steering metrics (friction feel, torque build-up and torque deadband) for two function parameters (basic steering torque and active return) of the EPAS. The optimisation method enabled all metrics to fall successfully within the target range.

A major challenge for the automotive industry is to reduce the development time while meeting quality assessments for their products. This calls for new design methodologies and tools that scale with the increasing amount and complexity of embedded systems in today's vehicles. In this paper we undertake an approach to embedded software design based on executable models expressed in the high-level modelling paradigm of Timber. In this paper we extend previous work on Timber with a multi-paradigm design environment, aiming to bridge the gap between engineering disciplines by multi-body co-simulation of vehicle dynamics, embedded electronics, and embedded executable models.