A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars

Road drivable area detection is an important task in computer vision with applications in self-driving cars. Accurately detecting and mapping drivable areas in a scene allow vehicles and robots to plan safe trajectories. In this paper, a novel cognitively inspired approach is proposed that considers both the salient areas in a driving scene and the driver's attention mechanism. Specifically, the attention point is computed by combining salient areas and attention regions. Furthermore, we use the attention point and two boundary nodes on the road edge to form a triangular road surface area. Finally, we segment this area and remove the salient region within this area to obtain the drivable road area. Experimental results show that our proposed method can address the shortcomings of traditional vanishing point detection algorithms and enhance drivable area perception when combined with 4 different backbones on the DeepLabV3+ model. In particular, we demonstrate the effectiveness of merging salient area and attention area algorithms and explore the joint understanding of these complementary visual cues.