High definition (HD) map has become indispensable to autonomous driving systems, but its automated construction is challenging. Since road boundary lines are basic elements of the map, accurate and complete extraction of the lines is one of key to build HD map. We present a method to automatically extract the boundary lines based on the road model using multi-beam LIDAR. First, the road model based on B-spline surfaces is constructed to fit the ground, and the multiRANSAC is proposed to remove the surface points. Second, horizontal distance feature and strong straight constraint are used to extract the road boundary lines. Finally, the lines collected from multiple frames are fused to approximate the curves and complete the boundary information. In the matter, the proposed method extracts the lines on the curved and straight road accurately and completely. We verified the feasibility and robustness of the proposed method on KITTI dataset. The average precision, recall and F1 are 0.9599, 0.9066 and 0.9277 respectively, and the average runtime is 28 $ms/frame$ . Results show that the comprehensive performance of the proposed method outperforms the compared approaches.

Vehicle mass is crucial to autonomous vehicles control. Affected by the nonlinearity of vehicle dynamics between vehicle states, it is still a tough issue to estimate vehicle mass precisely and stably. The transformer aided adaptive extended Kalman filter is proposed to further improve the accuracy and stability of estimation. Firstly, the transformer-based estimator is introduced to provide an accurate pre-estimation of vehicle mass, with the nonlinear dynamics among vehicle states being learned. Secondly, on the basis of comparing the real-time input and training data of neural network, the weight adjustment module is designed to present an adaptive law. Finally, the adaptive extended Kalman filter is proposed to meet the demand of accuracy and stability, where the pre-estimation of transformer-based estimator is integrated with the adaptive law. Dataset is collected by conducting heavy-duty vehicle simulation. The mean absolute percentage error, mean absolute error, root mean square error and convergence rate averaged over simulation tests are 0.90%, 256.47 kg, 357.01 kg and 184 steps, respectively. The results show the outperformance of the proposed method in terms of accuracy and stability.