We propose a novel, computationally efficient, and robust light detection and ranging (LiDAR)-only odometry framework based on truncated least squares termed T-LOAM. Our method focuses on alleviating the impact of outliers to allow robust navigation in sparse, noisy, or cluttered scenarios where degeneration occurs. As preprocessing, the multiregion ground extraction and dynamic curved-voxel clustering methods are proposed to accomplish the segmentation of 3D point clouds and filter out unstable objects. A novel feature extraction module is tailored to discriminate four peculiar features: edge features, sphere features, planar features, and ground features. As frontend, a hierarchical feature-based LiDAR-only odometry performs precise motion estimates through the truncated least squares method for directly processing various features. The preprocessing model and motion estimation precision have been evaluated on the KITTI odometry benchmark as well as various campus scenarios. The experimental results have demonstrated the real-time capability and superior precision of the proposed T-LOAM over other state-of-the-art algorithms.

As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a prominent role in the decision-making process of autonomous driving (AD), which enables autonomous vehicles (AVs) to learn an optimal driving strategy through continuous interaction with the environment. This paper proposes a deep reinforcement learning (DRL)-based motion planning strategy for AD tasks in the highway scenarios where an AV merges into two-lane road traffic flow and realizes the lane changing (LC) maneuvers. We integrate the DRL model into the AD system relying on the end-to-end learning method. An improved DRL algorithm based on deep deterministic policy gradient (DDPG) is developed with well-defined reward functions. In particular, safety rules (SR), safety prediction (SP) module and trauma memory (TM) as well as the dynamic potential-based reward shaping (DPBRS) function are adopted to further enhance safety and accelerate learning of the LC behavior. For validation, the proposed DSSTD algorithm is trained and tested on the dual-computer co-simulation platform. The comparative experimental results show that our proposal outperforms other benchmark algorithms in both driving safety and efficiency.

Planning for autonomous vehicles to merge into high‐density traffic flows within limited mileage is quite challenging. Specifically, the driving trajectory will inevitably have intersections with other vehicles whose driving intentions can't be directly observed. Herein, a two‐stage algorithm framework that is decomposed into the longitudinal and lateral planning processes for online merging planning is proposed. An improved particle filter is used to estimate the driving models of surrounding vehicles for predicting their future driving intentions. Based on Monte Carlo tree search (MCTS), different action spaces are evaluated for longitudinal merging gap selection and lateral interactive merging operation, while heuristic pruning is used to reduce the computation cost. Moreover, the coefficients related to the driving styles are introduced, and their influences on merging performance are analyzed. Finally, the proposed algorithm is implemented in a two‐lane simulation environment. The results show that the proposal has outperformed other baseline methods.