Open Access

Environmental perception is one of the biggest challenges in autonomous driving to move inside complex traffic situations properly. Perceiving the road's condition is necessary to calculate the drivable space; in manual driving, this is realized by the human visual cortex. Enabling the vehicle to detect road conditions is a critical and complex task from many perspectives. The complexity lies on the one hand in the development of tools for detecting damage, ideally using sensors already installed in the vehicle, and on the other hand, in integrating detected damages into the autonomous driving task and thus into the subsystems of autonomous driving. High-Definition Feature Maps, for instance, should be prepared for mapping road damages, which includes online and in-vehicle implementation. Furthermore, the motion planning system should react based on the detected damages to increase driving comfort and safety actively. Road damage detection is essential, especially in areas with poor infrastructure, and should be integrated as early as possible to enable even less developed countries to reap the benefits of autonomous driving systems. Besides the application in autonomous driving, an up-to-date solution on assessing road conditions is likewise desirable for the infrastructure planning of municipalities and federal states to make optimal use of the limited resources available for maintaining infrastructure quality. Addressing the challenges mentioned above, the research approach of this work is pragmatic and problem-solving. In designing technical solutions for road damage detection, we conduct applied research methods in engineering, including modeling, prototyping, and field studies. We utilize design science research to integrate road damages in an end-to-end concept for autonomous driving while drawing on previous knowledge, the application domain requirements, and expert workshops. This thesis provides various contributions to theory and practice. We design two individual solutions to assess road conditions with existing vehicle sensor technology. The first solution is based on calculating the quarter-vehicle model utilizing the vehicle level sensor and an acceleration sensor. The novel model-based calculation measures the road elevation under the tires, enabling common vehicles to assess road conditions with standard hardware. The second solution utilizes images from front-facing vehicle cameras to detect road damages with deep neural networks. Despite other research in this area, our algorithms are designed to be applicable on edge devices in autonomous vehicles with limited computational resources while still delivering cutting-edge performance. In addition, our analyses of deep learning tools and the introduction of new data into training provide valuable opportunities for researchers in other application areas to develop deep learning algorithms to optimize detection performance and runtime. Besides detecting road damages, we provide novel algorithms for classifying the severity of road damages to deliver additional information for improved motion planning. Alongside the technical solutions, we address the lack of an end-to-end solution for road damages in autonomous driving by providing a concept that starts from data generation and ends with servicing the vehicle motion planning. This includes solutions for detecting road damages, assessing their severity, aggregating the data in the vehicle and a cloud platform, and making the data available via that platform to other vehicles. Fundamental limitations in this dissertation are due to boundaries in modeling. Our pragmatic approach simplifies reality, which always distorts the degree of truth in the result. This affects the model building of the quarter-vehicle and deep learning. Further limitations occur in the end-to-end concept. This represents the integration of road damages in the autonomous driving task but does not detail the aggregation modules and interfaces of the subsystems. The completion of this work does not conclude the topic of road damage detection and assessment in autonomous driving. Research must continue to optimize the proposed solutions and test them on a widespread basis in the real world. Furthermore, the sensor fusion of different approaches is fascinating in order to combine the advantages of individual systems. Integrating the end-to-end concept into the ecosystem of an autonomous vehicle is another fascinating field, taking interfaces and cloud platforms into account.