To solve the problem of high memory and computational resource demands in obstacle detection models within autonomous driving perception domain controllers, a lightweight obstacle detection method based on improved YOLOv8 is proposed. This method reconstructs the YOLOv8 backbone network using FasterNet, which utilizes less memory access and computational resources. To mitigate the accuracy decline and the insufficient detection capabilities for small objects caused by model lightweighting, three main improvements are made to YOLOv8: SPD-Conv (space-to-depth convolution) is used to replace traditional stride convolution in the neck network to enhance small object feature extraction; IP-IoU, combining the concepts of Inner-IoU and Powerful-IoU, is introduced as the bounding box regression loss to accelerate loss convergence and improve small object detection performance; and the SimAM (simple attention module) is incorporated to further enhance model detection accuracy. Experimental results demonstrate that, compared to the original model, the improved model achieves a reduction of 29.1% in parameters, 20.5% in computational load, and 28.8% in model size, while increasing mAP@0.5 by 1.2%. Once deployed in autonomous driving vehicle controllers, the model effectively detects obstacles on the road ahead.