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Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

A gray-box optimization procedure based on evolutionary algorithms for the initial design of a suspension concept for four wheel independently driven and steered vehicles is developed. With the presented optimization method, the energy consumption together with state of the art knowledge about the parametrization and design of vehicle suspension systems leads to an optimization setup closely to real world requirements while the vehicle’s topology is exploited. To this, the modelling presented in [1] is considered as a geometric suspension model. Furthermore, to take advantage of the potential of such vehicles, an autonomous closed-loop setup with integrated motion control is utilized. During the optimization, the chassis parameters with the most impact on energy consumption and driving dynamics, namely camber, caster, scrub radius and the steering axis inclination (SAI) depending on a varying caster angle and SAI in relation to the steering angle, will be focused.

A procedure for the initial design of a suspension concept with four independently driven and steered wheels is developed, whereby, steering angles above conventional values are considered. To fully exploit the potential of such vehicles, an autonomous closed-loop setup with integrated motion control is utilized. The goal is to obtain statements for an optimal suspension design and parametrization maintaining a general approach, while underlying black-box control and the vehicle configuration remains exchangeable. The investigation of the influence of the chassis parameters, with crucial impact on energy consumption, comfort and driving dynamics, namely camber, caster, scrub radius and the steering axis inclination (SAI) depending on a varying caster angle and SAI in relation to the steering angle will be focused. For this sensitivity analysis, an explicit behavioral-oriented model of the suspension is created.

Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundreds of millions of test kilometers without incidents to show that statistically HAD is significantly safer. One approach is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenarios is fused in a world model and enhanced by incorporating future time steps into original measurements.

Brake pedal feedback is important for driver's perception during the driving task as well as the pedal feel is an important factor in customer satisfaction. Therefore, a force emulation device is beneficial during the design phase to evaluate the pedal characteristic. Such a system is also needed for driving simulators. Usually, brake feedback systems in simulators rely on passive elements like springs and dampers to emulate the force. This does not allow the implementation of an arbitrary nonlinear pedal force characteristic. In this paper we propose an active pedal feedback simulator which can emulate an arbitrarily customizable and online adjustable brake pedal characteristic. The particular advantage of our pedal simulator is that the system can also emulate the exact pedal dynamics. This is advantageous compared to other active brake feedback simulators which rely on hydraulic actors.