Objective The traffic driving perception system is a critical component of autonomous driving technology. It involves multiple tasks and requires high real-time performance and reliability. Existing methods suffer from issues such as poor detection performance for small objects and imprecise semantic segmentation of target regions under partial occlusion. To address these problems an end-to-end multi-task network model called MTNet is proposed to accomplish two critical tasks in the traffic driving perception system drivable area segmentation and lane line detection. Methods MTNet improved the neck network design by enhancing the feature pyramid network FPN module enabling more efficient detection performance. The network architecture incorporated the efficient layer aggregation network ELAN structure and reparameterization techniques to upgrade the original convolutions. Moreover precise multi-task loss functions were designed based on the characteristics of each task and an effective training strategy was designed. Results Extensive experiments conducted on the BDD100k Berkeley DeepDrive dataset demonstrated the effectiveness of MTNet. The drivable area segmentation task achieved an average intersection over union IoU of 95. 6 % while the lane line detection task achieved an accuracy of 89. 3 %. Conclusion The experimental results show that MTNet exhibits robustness against complex background interference and improves the accuracy of object detection and segmentation. The model achieves good detection and segmentation performance in various scenarios whether during the day or at night and in both complex and simple environments.