Road Construction Site Detection using Low-Level Sensor Fusion for Self-Driving Cars

Navigating through road work zones remains a challenge for the development of autonomous driving technology. While HD maps are essential for accurate localization and navigation in autonomous vehicles, they face issues when encountering dynamic and constantly changing situations on the road such as road construction sites. As a result, autonomous vehicles need to rely on their onboard sensor data for safe navigation through construction zones. This thesis focuses on low-level fusion-based methods, using both point cloud and image data for the detection of road construction sites. The primary objective is to identify temporary traffic control devices like delineator posts, safety barriers, and traffic cones which play a role in ensuring road safety while maintaining smooth traffic flow throughoutconstruction areas.
To achieve this, the CARLA simulator is used to generate an autonomous driving dataset that represents various road construction sites that are frequently observed in German regions. This dataset forms the basis for evaluating four state-of-theart low-level LiDAR-camera fusion-based methods. By establishing a benchmark, this thesis presents a proof of concept for successful road work zone detection. The results demonstrate the effectiveness of low-level fusion-based methods in identifying road construction sites and open the door for further developments, emphasizing its potential impact on advancing autonomous driving technology within work zones.