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Due to current progresses in the field of driver assistance systems and the continuously growing electrification of vehicle drive trains, the evaluation of driver behavior has become an important part in the development process of modern cars. Findings from driver analyses are used for the creation of individual profiles, which can be permanently adapted due to ongoing data processing. A benefit of data-based dynamic control systems lies in the possibility to individually configure the vehicle behavior for a specific driver, which can contribute to increasing customer acceptance and satisfaction. In this way, an optimization of the control behavior between driver and vehicle and the resulting mutual system learning and -adjustment hold great potential for improvements in driving behavior, safety and energy consumption.

The replacement of safety-critical mechanical components with electro-mechanical systems has led to the fact that safety aspects play a central role in development of embedded automotive systems. Recently, consumer demands for connectivity (e.g., infotainment, car-2-car or car-2-infrastructure communication) as well as new advances toward advanced driver assistance systems (ADAS) or even autonomous driving functions make cybersecurity another key factor to be taken into account by vehicle suppliers and manufacturers. Although these can capitalize on experiences from many other domains, they still have to face several unique challenges when gearing up for specific cybersecurity challenges. A key challenge is related to the increasing interconnection of automotive systems with networks (such as Car2X). Due to this connectivity, it is no longer acceptable to assume that safety-critical systems are immune to security risks.

The present technical article deals with the modeling of dynamic tire forces, which are relevant during interactions of safety relevant Advanced Driver Assistance Systems (ADAS). Special attention has been paid on simple but effective tire modeling of semi-physical type. In previous investigations, experimental validation showed that the well-known first-order Kelvin-Voigt model, described by a spring and damper element, describes good suitability around fixed operation points, but is limited for a wide working range. When aiming to run vehicle dynamics models within a frequency band of excitation up to 8 Hz, these models deliver remarkable deviations from measured tire characteristics. To overcome this limitation, a nonlinear Maxwell spring-damper element was introduced which is qualified to model the dynamic hardening of the elastomer materials of the tire.

Advanced Driver Assistance Systems (ADAS) for collision avoidance/mitigation have already demonstrated their benefit on vehicle safety. Often those systems have an additional functionality for comfort to assist the driver in non-critical driving. The verification of ADAS functionality using different test scenarios is currently investigated in many different projects worldwide. A harmonization of test scenarios and evaluation criteria is not yet accomplished. Often, these test scenarios focus on objective collision avoidance and not on the subjective interaction between driver and vehicle. The present study deals with the development of an experimental validation plan for the systems Automatic Cruise Control (ACC), Lane Departure Warning (LDW) and Lane Keeping Assist (LKA). Standardized driving maneuvers with two or more vehicles equipped with synchronized measurement are performed by professional test drivers.