To address the accuracy-speed trade-off in semantic segmentation for resource-constrained driver assistance systems, this study proposes EAANet (Edge-Aware Attention Network). Building upon a Lightweight Progressive Scalable Network (LPSNet) as the baseline, we first embed Squeeze-and-Excitation (SE) channel attention into feature fusion layers to establish channel-wise adaptive feature selection. The Atrous Spatial Pyramid Pooling (ASPP) module is then enhanced through four parallel branches for improved multi-scale feature extraction. Further optimizations include reconstructing the decoder with depthwise separable convolutions and compressing the model size via parameter pruning. Experiments on the Cityscapes dataset demonstrate that EAANet achieves 76.7% mIoU, surpassing LPSNet by 3.2% points while reducing parameters to 1.2M. When deployed on NVIDIA Jetson Xavier NX edge devices, it attains real-time inference at 33.5 frames per second, with specific accuracy improvements of 9.0% for vehicles and 14.7% for traffic signs. The proposed model significantly enhances critical object recognition while maintaining real-time performance, offering a cost-effective semantic segmentation solution for vehicular edge computing platforms.