Open Access

The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions.

Since the end of 2022, ChatGPT and other chatbots continue to revolutionise the way we work, and especially in scientific and academic fields, this is both an opportunity and a challenge for both students and lecturers. Custom chatbots can also be easily created to suit specific applications and purposes. In addition, there are many other AI tools, e.g.,for image and video generation and manipulation, scientific work or presentations. This study evaluates some of these AI tools besides ChatGPT with regard to different applications for academic purposes, both from the students' and lecturers' point of view. As part of an interdisciplinary project, students, in collaboration with the lecturer, selected several AI tools based on possible application scenarios and investigated the basics and functionalities of the tools. In the next step, they analysed these tools with regard to possible teaching and learning scenarios. Based on these scenarios, they used the tools to develop detailed applications in different teaching-learning scenarios to check the suitability, effectiveness and correctness and to evaluate the results.Teaching material of a dedicated lecture, including presentations, videos, textbook and more was used as demonstrator for the different AI tools.