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During the last years mechatronic systems developed into one of the biggest drivers of innovation in the automotive industry. The start of production of systems like dual clutch transmission, lane departure warning systems and active suspensions proves this statement. These systems have an influence on the longitudinal, steering and vertical dynamics of the vehicle. That is why the interaction on vehicle level is crucial for an optimal result in the fields of efficiency, comfort, safety and dynamics. To optimize the interaction of mechatronic systems, in this paper a new test rig concept for a complete vehicle is presented. The so-called Car-in-the-Loop-concept is capable of realistically reproducing the loads, which act on the powertrain, the steering and the suspension during a test drive.

Recently, the European Union has adopted a new regulation on Real-Driving-Emissions (RDE) and also China is considering RDE implementation into new China 6 legislation. The new RDE regulation is focused on measuring nitrogen oxides (NOx) and particulate number (PN) emissions of both light-duty gasoline and diesel vehicles under real world conditions. A supplemental RDE test procedure was developed for European type approval, which includes on-road testing with cars equipped with portable emission measurement systems (PEMS). This new regulation will significantly affect the engine calibrations and the exhaust gas aftertreatment. In this study the impact of the new RDE regulation on two recent EU 6b certified turbocharged direct injected gasoline vehicles has been investigated. A comparison of several chassis dyno drive cycles with two new defined on-road RDE cycles was performed.

In an age of growing complexity with regards to vehicle control systems, verification and validation of control algorithms is a rigorous and time consuming process. With the help of rapid control prototyping techniques, designers and developers have cost effective ways of validating controls under a quicker time frame. These techniques involve developments of plant models that replicate the systems that a control algorithm will interface with. These developments help to reduce costs associated with construction of prototypes. In standard design cycles, iterations were needed on prototypes in order to finalize systems. These iterations could result in code changes, new interfacing, and reconstruction, among other issues. The time and resources required to complete these were far beyond desired. With the help of simulated interfaces, many of these issues can be recognized prior to physical integration.

Indoor laboratory tire testing on flat belt machines and tire testing on the actual road yield different results. Testing on the machine offers the advantage of repeatability of test conditions, control of the environmental condition, and performance evaluation at extreme conditions. However, certain aspects of the road cannot be reproduced in the laboratory. It is thus essential to understand the connection between the machine and the road, as tires spend all their life on the road. This research, investigates the reasons for differences in tire performance on the test machine and the road. The first part of the paper presents a review on the differences between tire testing in the lab and on the road, and existing methods to account for differences in test surfaces.

Effectively determining automated driving system (ADS)-equipped vehicle (AV) safety without relying on testing an infeasibly large number of driving scenarios is a challenge with wide recognition in industry and academia. The following paper builds on previous work by the Institute of Automated Mobility (IAM) and Science Foundation Arizona (SFAz), and proposes a test selection and scoring methodology (TSSM) as part of a safety case-based framework being developed by the SFAz to ensure the safety of AVs while addressing the scenario testing challenge. The TSSM is an AV verification and validation (V&V) process that relies, in part, on iterative, partially random generation of AV driving scenarios. These scenarios are generated using an operational design domain (ODD) and behavioral competency portfolio, which expresses the vehicle ODD and behavioral competencies in terms of quantifiable amounts or intensities of discrete components.