Steering angle control is crucial for the safe operation of autonomous vehicles, as even a minor error can lead to serious traffic accidents. However, many end-to-end steering angle prediction systems overlook the extraction of task-relevant features. Including irrelevant features such as trees, buildings, and the sky can significantly reduce the performance of steering angle prediction. To address this issue, we propose TLNBm-NCP, a new steering angle prediction network that utilizes drivable region segmentation and bio-inspired neural circuit policy. Specifically, TLNBm-NCP employs TwinLiteNet to Generate images containing both drivable and non-drivable areas. Then, a binary mask and a multi-scale spatio-temporal feature extraction module (Multc) are introduced to extract the features of the lane regions. Finally, the bio-inspired neural circuit policies (NCPs) is employed to learn temporal dependencies between image frames and provide the steering angle. After extensive experiments on Udacity, Carla, and SullyChen datasets, TLNBm-NCP demonstrates superior performance compared to existing models. This model shows promise for autonomous driving steering angle prediction. The code and supplementary materials are available at https://github.com/wyl121/TLNBm-NCP .