For autonomous vehicles, it is an important requirement to obtain integrate static road information in real-time in dynamic driving environment. A comprehensive perception of the surrounding road should cover the accurate detection of the entire road area despite occlusion, the 3D geometry and the types of road topology in order to facilitate the practical applications in autonomous driving. To this end, we propose a lightweight and efficient LiDAR-based multi-task road perception network (LMRoadNet) to conduct occlusion-free road segmentation, road ground height estimation, and road topology recognition simultaneously. To optimize the proposed network, a corresponding multi-task dataset, named MultiRoad, is built semi-automatically based on the public SemanticKITTI dataset. Specifically, our network architecture uses road segmentation as the main task, and the remaining two tasks are directly decoded on a concentrated 1 / 4 scale feature map derived from the main task's feature maps of different scales and phases, which significantly reduces the complexity of the overall network while achieves high performance. In addition, a loss function with learnable weight of each task is adopted to train the neural network, which effectively balances the loss of each task and improves performance of the individual tasks. Extensive experiments on the test set show that the proposed network achieves great performance of the three tasks in real-time, outperforms the conventional multi-task architecture and is comparable to the state-of-the-art efficient methods. Finally, a fusion strategy is proposed to combine results on different directions to expand the field of view for practical applications.

Preceding vehicles have a significant impact on the safety of the vehicle, whether or not it has the same driving direction as an ego-vehicle. Reliable trajectory prediction of preceding vehicles is crucial for making safer planning. In this paper, we propose a framework for trajectory prediction of preceding target vehicles in an urban scenario using multi-sensor fusion. First, the preceding target vehicles historical trajectory is acquired using LIDAR, camera, and combined inertial navigation system fusion in the dynamic scene. Next, the Savitzky–Golay filter is taken to smooth the vehicle trajectory. Then, two transformer-based networks are built to predict preceding target vehicles’ future trajectory, which are the traditional transformer and the cluster-based transformer. In a traditional transformer, preceding target vehicles trajectories are predicted using velocities in the X-axis and Y-axis. In the cluster-based transformer, the k-means algorithm and transformer are combined to predict trajectory in a high-dimensional space based on classification. Driving data from the real-world environment in Wuhan, China, are collected to train and validate the proposed preceding target vehicles trajectory prediction algorithm in the experiments. The result of the performance analysis confirms that the proposed two transformers methods can effectively predict the trajectory using multi-sensor fusion and cluster-based transformer method can achieve better performance than the traditional transformer.