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Software will lead the development and life cycle of vehicles in the future. Nowadays, more and more software is being integrated into a vehicle, evolving it into a Software-Defined Vehicle (SDV). Automotive High Performance Computers (HPCs) serve as enablers by providing more computing infrastructure which can be flexibly used inside a vehicle. However, this leads to a complex vehicle system that needs to function today and in the future. Detecting and rectifying failures as quickly as possible is essential, but existing diagnostic approaches based on Diagnostic Trouble Codes (DTCs) are not designed for such complex systems and lack of flexibility. DTCs are predefined during vehicle development and changes to vehicle diagnostics require a large amount of modification work. Moreover, diagnostics are not intended to handle dynamically changing software systems and have shortcomings when applied to in-vehicle software systems.

The automotive world is progressing fast towards autonomous vehicles making sensors one of the critical components. There is a requirement for constant exchange of information between the vehicle and its surrounding environment, which is assisted by sensors such as Camera, LiDAR, and RADAR. However, exposure to harsh environmental conditions such as rain, dirt, snow, and bird droppings can hamper the functioning of the sensors and in turn interrupt accurate vehicle maneuvers. Sensor-cleaning mechanisms are required to be tested under various weather conditions and vehicle operating situations. Besides wind tunnel tests, digitalizing this whole process becomes important to take decision on design changes in early vehicle development stage. This work presents a digital methodology to test the LiDAR cleaning system in the advent of mud clearing at different vehicle speeds. The cleaning mechanism consists of a telescopic nozzle placed above the LiDAR translating back and forth.

More and more applications (apps) are entering vehicles. Customers would like to have in-car apps in their infotainment system, which they already use regularly on their smartphones. Other apps with new functionalities also inspire vehicle customers, but only as long as the customer can utilize them. To ensure customer satisfaction, it is important that these apps work and that failures are found and corrected as quickly as possible. Therefore, in-car apps also implicate requirements for future vehicle diagnostics. This is because current vehicle diagnostic methods are not designed for handling dynamic software failures of apps. Consequently, new diagnostic methods are needed to support the diagnosis of in-car apps. Log data are a central building block in software systems for system health management or troubleshooting. However, there are different types of log data and log environment setups depending on the underlying system or software platform.

The automotive industry changes rapidly. New players, concepts, and technologies from the Information Technology (IT) domain enter the market and software receives a high priority. Inside the vehicle, the number of components, which consist mostly of software, are increasing and more and more software-based functions are offered. In addition, High Performance Computers (HPCs) are continuing to be integrated into vehicles. These aspects lead to several challenges with current vehicle diagnostics, but also enable new opportunities in that field. However, in the specific area of vehicle diagnostics, there exists only very limited literature that considers current challenges and new possibilities for future vehicle diagnostics. Some literature deals with the general automotive system design or shows results from about five years ago. The viewpoints of an Original Equipment Manufacturer (OEM) are not included there.

Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system.

Over the last 40 years it has been possible to lengthen recommended passenger car engine oil drain intervals by up to five times, despite the substantial increases in oil stress through continously rising demands on performance and environmental acceptability. Behind this considerable progress lie improvements in engine design and production technology and the development of suitable advanced engine oil formulations. With increasing oil drain intervals comes a growing uncertainty as to exactly when the oil change should best be made: a fixed mileage applicable to all vehicles is preferred for its practicality but the optimum depends on the driving history of individual vehicles. In Europe a 15000 km oil drain interval is now normal. A further extension based on a fixed interval would give an advantage to a minority of customers but could seriously compromise the durability of engines in the overall vehicle population.