Vision-based Driver Assistance Systems for Teleoperation of OnRoad Vehicles : Compensating for Impaired Visual Perception Capabilities Due to Degraded Video Quality

Sammanfattning: Autonomous vehicles is going to be a part of future transport of goods and people, but to make them usable in unpredictable situations presented in real traffic, there is need for backup systems for manual vehicle control. Teleoperation, where a driver controls the vehicle remotely, has been proposed as a backup system for this purpose. This technique is highly dependent on stable and large wireless network bandwidth to transmit high resolution video from the vehicle to the driver station. Reduction in network bandwidth, resulting in a reduced level of detail in the video stream, could lead to a higher risk of driver error. This thesis is a two part investigation. One part looking into whether lower resolution and increased lossy compression of video at the operator station affects driver performance and safety of operation during teleoperation. The second part covers implementation of two vision-based driver assistance systems, one which detects and highlights vehicles and pedestrians in front of the vehicle, and one which detects and highlights lane markings. A driving test was performed at an asphalt track with white markings for track boundaries, with different levels of video quality presented to the driver. Reducing video quality did have a negative effect on lap time and increased the number of times the track boundary was crossed. The test was performed with a small group of drivers, so the results can only be interpreted as an indication toward that video quality can negatively affect driver performance. The vision-based driver assistance systems for detection and marking of pedestrians was tested by showing a test group pre-recorded video shot in traffic, and them reacting when they saw a pedestrian about to cross the road. The results of a one-way analysis of variance, shows that video quality significantly affect reaction times, with p = 0.02181 at significance level α = 0.05. A two-way analysis of variance was also conducted, accounting for video quality, the use of a driver assistance system marking pedestrians, and the interaction between these two. The results point to that marking pedestrians in very low quality video does help reduce reaction times, but the results are not significant at significance level α = 0.05.