Light detection and ranging (LiDAR) positioning has received great attention especially when satellites fail. However, the positioning accuracy is still subjected to the following. First, the LiDAR positioning accuracy is affected by the sparsity due to LiDAR beams and environment. Second, the existing methods for suppressing cumulative errors are based on ego-vehicle sensing that requires the motion of repeated paths. Moreover, the output frequency of LiDAR is low. To solve the above problems, an enhanced-LiDAR/ultrawideband (UWB)/inertial navigation system integrated positioning methodology is proposed. First, the enhanced LiDAR odometry module is designed to improve the resolution of LiDAR beams for more accurate odometry. Then, the cooperative optimization module is proposed to introduce UWB observation to suppress the accumulated error without relying on ego-vehicle sensing. Finally, the factor graph fusion module is used to fuse multisensor information dynamically and improve the output frequency. Experimental results prove the effectiveness of our methodology.

Place recognition plays a crucial role in simultaneous localization and mapping. Unfortunately, however, changes in viewpoints and conditions in large-scale environments impose tricky challenges for PR. To this end, this article specifically proposes an explicit points-of-interest driven PR method, which consists of a road segmentation module based on grid-wise patch U-transformer and a PR module based on regions of interest siamese transformer NetVLAD (RI_STV). Especially for RI_STV, in the individual dimension, it is dedicated to exploring the local topological features of nonroad regions of interest. In the spatial dimension, an improved Transformer is introduced to capture the global interactions between features of interest. In the cluster dimension, NetVLAD embedded with weighted pooling is created to perform weighted aggregation of feature clusters to generate discriminative and general descriptors. Evaluation on various datasets shows that our customized method is not only impressively competitive, but also strikes the best balance between accuracy and real-time performance.

The safety and efficiency of the merging zone is particularly important for traffic networks. Although autonomous vehicle improves the safety and efficiency from vehicle view, traffic controlling in merging zone mostly focus on improving efficiency from roadside view. Lacking of detailed driving recommendation, it ignores the safety of merging zone where commercial vehicle pose a high collision risk in real traffic. This paper proposes a roadside decision-making methodology to simultaneously improve the safety and efficiency of merging zone. We have built two modules, namely assessment and decision-making. Assessment module takes advantage of Bayesian inference to evaluate dynamic collision risk. Decision-making module based on deep reinforcement learning recommends the actions to commercial vehicles by roadside unit. A series of typical simulation tests show that our method increases the TTC of commercial vehicles by an average of 62.7%. In the free flow, the overall travel time of vehicles in the merging zone is reduced by 11.68%. Most notably, when congestion occurred, the average jam length is reduced by 59.68% on the premise of safety. Moreover, the average accuracy of the roadside decision-making method on the evaluation metrics of TTC, travel time, and jam length are 93.73%, 91.65%, and 94.45%, respectively. The experimental results show that the roadside decision-making methodology simultaneously improves safety and efficiency, and it dynamically adapts free and congested traffic flow.

Driving decision-making determines the safety and rationality of autonomous commercial vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision decision-making methodology based on deep reinforcement learning (RAD-DRL) was creatively proposed. Firstly, aiming at the dynamic coupling of rear anti-collision factors for safe driving, a driving decision network based on an actor-critic framework was proposed for sensor data processing. Then, inspired by the idea of multi-objective optimization, a refined reward function is developed. It comprehensively considers the impact of backward target types, safety clearance, and vehicle roll stability on the rear collision. Finally, the RAD-DRL was trained (with different values of random seeds), tested, and verified in a simulation environment where road network and traffic situations were built-in SUMO (Simulation of Urban Mobility). After 30,000 training episodes, effective and reliable rear anti-collision driving decisions were achieved by our proposed RAD-DRL. The simulated results indicated that the RAD-DRL has remarkable superiority in generalization and effectiveness in expressway scenarios with different driving cases. Especially, it maintained good decision performance in the corner case in which the backward vehicle suddenly accelerates.

The dynamic interaction among Connected and Automated Vehicles (CAVs) is becoming increasingly complex, encompassing factors such as dynamic topology and the dynamic states of multiple CAVs. Existing cooperative control methods struggle to explicitly represent dynamic interaction, which can lead to dangerous behavior, severe congestion, and even accidents. In this paper, we propose a cooperative control methodology that aims to improve safety and efficiency in the merging zone by deeply representing dynamic interaction among multiple CAVs. Our proposed methodology, named GMA-DRL, utilizes a spatial graph convolutional encoder with a multi-head attention mechanism to explicitly represent dynamic interaction among vehicles. Furthermore, deep reinforcement learning based on Actor-Critic with temporal relation regularization is utilized to ensure the consistency of dynamic interaction and generate cooperative driving actions for multiple CAVs. The GMA-DRL is tested in a series of typical merging scenarios with dynamic interaction. Extensive experimental results show that the GMA-DRL outperforms the existing cooperative control models in term of headway, average speed and acceleration. It demonstrates that the GMA-DRL with explicitly represent dynamic interaction can improve safety and efficiency of multiple CAVs in the merging zone.

AbstractIn complex environments with long-term changes such as light, seasonal and viewpoint changes, robust, accurate and high-frequency global positioning based on LiDAR map is still a challenge, which is crucial for autonomous vehicles or robots. To this end, a novel observation model that relies on the siamese multi-task Convolutional Neural Networks (CNNs) with multi-module cascaded is creatively presented in this paper. In particular, a new pseudo image representing LiDAR submap is designed to enrich scene texture and enhance rotation invariance. Besides, a novel siamese CNNs that coupled by NeXtVLAD and Long Short-Term Memory (LSTM) is designed for the first time, which can reliably predict similarity and quaternion at the same time. Finally, the predicted quaternion observation is integrated into the extended Kalman filter framework for multi-sensor fusion to achieve robust high-frequency global pose estimation. Extensive evaluations on KITTI, NCLT, and real-world datasets suggest that the proposed method not only obtains the remarkable precision-recall performance, but also effectively enhances and improves the robustness and accuracy of long-term positioning.