Cost-Sensitive Road Obstacle Detection for Low-Cost Autonomous Vehicles

Road obstacle detection is one of the crucial tasks for the safe and efficient operation of autonomous vehicles. Existing detection methods often try to detect all objects in the image (i.e. a frame in a video stream). However, it is important to note that not all objects are equally dangerous and that the detection system should not react equally to them. To reduce computation costs (e.g. time and power consumption) in a mobile device, this study introduces a cost-sensitive obstacle detection method to leverage the performance of YOLIC (You Only Look at Interested Cells), a method proposed by us previously. The new method allows YOLIC to pay more attention to important areas for driving. In the experiments, we assign high weights to areas close to the vehicle, and the weights vary depending on the detection distance, direction, and relation to driving. Experimental results on two road obstacle datasets demonstrate that the proposed cost-sensitive detection method can effectively reduce the costs in the most dangerous areas of a given image compared with the baseline YOLIC model. Moreover, our fastest model can achieve real-time performance on a Raspberry Pi 4B, making it possible to deploy the proposed method in low-cost vehicles such as scooters and delivery robots.