Ted Ding

Research project title: 
Adaptive Sensor Fusion for Resilient Vehicle Sensing
Principal goal for project: 
To develop novel multi-modal sensor fusion methodologies for more robust scene understanding in adverse weather conditions. Such scene understanding tasks include object detection, semantic segmentation and surface reconstruction. The sensors that will be exploited are radar, lidar and optical camera.
Research project: 

To comprehensively perceive the surrounding environment, current advanced driver-assistance systems are usually equipped with a suite of complementary sensors, including radar, lidar and optical camera. Moreover, it is crucial for such a system to function robustly in a range of adverse weather conditions (e.g. fog, rain and snow). To this end, data from these disparate
sensors needs to be fused in a more effective manner to fully exploit their complementarity. We expect the success of this project to help progress the level of driving automation, particularly in the aforementioned complicated scenes.

Student type: 
Current student