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In this paper, an autonomous driving lateral control algorithm is introduced for bus rapid transit (BRT, with an articulated bus). The control algorithm includes an integration of model predictive control (MPC) for lateral control and PID control algorithms for longitudinal control considering the vehicle properties. To verify the algorithm in real-time, a model-in-the-loop system was developed using TruckSim in NI Veristand and Matlab/Simulink in Micro-AutoBox. In TruckSim, a BRT plant model was verified using vehicle tests and then applied. In addition, GPS data from a BRT route were acquired from the vehicle test and applied to a TruckSim scenario. In the Matlab/Simulink, the autonomous driving lateral control algorithm for MPC was developed. To communicate between the plant model and the control algorithm in real-time, a controller area network (CAN) protocol was defined and applied like a real vehicle. Therefore, a real-time verification environment was prepared to test the real-time autonomous driving lateral control algorithm for BRT. By using the verification environment, the control algorithm was verified using the ISO-11270 standard. Although many efforts have been made to develop an autonomous driving control algorithm, autonomous driving of BRT is expected more tangible because the BRT has its own lane and speed limitation. Therefore, this paper will introduce about study of real time feasibility, adaptation of vehicle variables, and precise prediction model for MPC algorithm. [ABSTRACT FROM AUTHOR]

For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving. [ABSTRACT FROM AUTHOR]

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant development in unmanned platform technologies, specifically unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). As a result, their application scenarios have expanded considerably. Unmanned platforms are considered integral components of the Internet of Things system. However, certain challenges arise when dealing with specialized tasks, such as navigating complex urban low-altitude terrain with multiple obstacles and limited communication capabilities. These challenges can greatly impact the efficiency of the system due to information isolation. To address this issue, a messenger drone mechanism is introduced in this paper, which utilizes air superiority to facilitate indirect communication between unmanned platforms. Additionally, a task sequence planning algorithm based on sampling transformation is designed. This algorithm efficiently assigns the drone to mobile UGVs by discretely sampling their paths and considering the UAV-UGV motion relationship. By transforming the problem into an asymmetric traveler problem, it allows for a fast solution. Finally, the effectiveness of the algorithm is verified through comparative analysis in different scenarios. [ABSTRACT FROM AUTHOR]

Three-colour four-way traffic light completed 100 years in 2020. Even though the traffic light in the form of Semaphore arms has been in use in London since 1868, electric traffic lights came into existence in 1912 and the standard three-colour four-way light in 1920. Research is continuously being carried out to develop better algorithms to improve safety, reduce travel delays, and optimize road capacity. Hence a review of the evolution of traffic lights is warranted. This paper presents an all-round review using a six-prong approach. Timeline of the evolution of the literature in the last 100 years, the evolution of hardware, algorithms, traffic control schemes, standards and the pedestrian lights and count down timer are the six areas in which the review is carried out. A timeline of the different keywords related to the various algorithms in use is presented. This article delves into the thinking and meticulous approach of early researchers and practitioners of the field while dwelling on the past. They laid the rock-solid foundation of today's research. Also, future research areas like connected vehicles and automated vehicles are pointed out, and a summary of the findings is presented at the end. [ABSTRACT FROM AUTHOR]

To overcome the challenges of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) in autonomous flights, this paper proposes the Dual Experience Attention Convolution Soft Actor-Critic (DAC-SAC) algorithm. This algorithm integrates a dual experience buffer pool, a self-attention mechanism, and the Soft-Actor-Critic algorithm with a convolutional network. The dual experience buffer pools are used to solve the problem of ineffective UAV training due to the scarcity of successful training data. To overcome the drawbacks of the original Soft Actor-Critic (SAC) algorithm in handling image data, a Convolutional Neural Network (CNN) is applied to reconstruct the actor and critic network, allowing for better image feature extraction and classification. Furthermore, a self-attention mechanism is employed by adding a convolutional self-attention layer to the network. This modification enables dynamic adjustments for the attention weights based on varying input image features, effectively addressing focus-related challenges. Two simulation experiments are performed and the DAC-SAC algorithm achieves a 99.5% success rate in a known environment and an 84.8% success rate when dealing with an unknown environment. These results confirm that the proposed algorithm enables autonomous obstacle avoidance for UAVs even when considering depth images as input. [ABSTRACT FROM AUTHOR]

Shared control algorithms have emerged as a promising approach for enabling real-time driver automated system cooperation in automated vehicles. These algorithms allow human drivers to actively participate in the driving process while receiving continuous assistance from the automated system in specific scenarios. However, despite the theoretical benefits being analyzed in various works, further demonstrations of the effectiveness and user acceptance of these approaches in real-world scenarios are required due to the involvement of the human driver in the control loop. Given this perspective, this paper presents and analyzes the results of a simulator-based study conducted to evaluate a shared control algorithm for a critical lateral maneuver. The maneuver involves the automated system helping to avoid an oncoming motorcycle that enters the vehicle's lane. The study's goal is to assess the algorithm's performance, safety, and user acceptance within this specific scenario. For this purpose, objective measures, such as collision avoidance and lane departure prevention, as well as subjective measures related to the driver's sense of safety and comfort are studied. In addition, three levels of assistance (gentle, intermediate, and aggressive) are tested in two driver state conditions (focused and distracted). The findings have important implications for the development and execution of shared control algorithms, paving the way for their incorporation into actual vehicles. [ABSTRACT FROM AUTHOR]

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims to reduce human errors that are the reason for about 95% of car accidents. The ADS consists of six stages: sensors, perception, localization, assessment, path planning, and control. We explain the main state-of-the-art techniques used in each stage, analyzing 275 papers, with 162 specifically on path planning due to its complexity, NP-hard optimization nature, and pivotal role in ADS. This paper categorizes path planning techniques into three primary groups: traditional (graph-based, sampling-based, gradient-based, optimization-based, interpolation curve algorithms), machine and deep learning, and meta-heuristic optimization, detailing their advantages and drawbacks. Findings show that meta-heuristic optimization methods, representing 23% of our study, are preferred for being general problem solvers capable of handling complex problems. In addition, they have faster convergence and reduced risk of local minima. Machine and deep learning techniques, accounting for 25%, are favored for their learning capabilities and fast responses to known scenarios. The trend towards hybrid algorithms (27%) combines various methods, merging each algorithm's benefits and overcoming the other's drawbacks. Moreover, adaptive parameter tuning is crucial to enhance efficiency, applicability, and balancing the search capability. This review sheds light on the future of path planning in autonomous driving systems, helping to tackle current challenges and unlock the full capabilities of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Achieving accurate and real-time perception of environmental targets in complex traffic scenes based on visual sensors is a challenging research problem in the field of autonomous driving technology. In methods to date, it is difficult to effectively balance the detection accuracy and speed. To this end, this paper proposes an interactive and lightweight visual detection algorithm – YRDM (Your Region Decision-Making) – based on the concepts of efficient mining and utilisation of target feature information, lightweight network structure, and optimisation of label allocation for highly practical detection of ambient targets in autonomous driving scenarios. First, a two-stage algorithm architecture consisting of four low-parameter subnetworks is constructed with the goal of efficiently mining and utilising target feature information, and the accuracy and effectiveness of the algorithm are balanced through the interaction of information between the subnetworks. Second, in order to further improve the detection speed, lightweight convolution is introduced into the structure of the YRDM network to construct the DSC3 module, which allows lightweight processing of the subnetwork structure. Finally, by converting the label assignment problem into an optimal transport problem, adaptation to the global nature of the samples by YRDM is improved, allowing better detection accuracy. The algorithm is tested with two major public datasets, BDD100K and KITTI, and a large number of experimental results show that the comprehensive performance of YRDM is better than other existing algorithms. In addition, ablation experiments and mobile terminal device deployment experiments further demonstrate the effectiveness and real-time performance of this algorithm. • Proposed a two-stage detection algorithm called your region decision-making (YRDM). • YRDM addressed challenges of complex traffic and limited computing resources. • YRDM achieved 57.8 % accuracy (BDD100K); 89.6 % (KITTI) with 2.98 GFLOPs. • Info interaction in low-parameter subnets boosts accuracy. • Lightweight convolution and DSC3 enhanced speed and efficiency. [ABSTRACT FROM AUTHOR]

Aiming at the problem that Ant Colony Optimization (ACO) is subject primarily to the parameters, we propose a hybrid algorithm SOA-ACO-2Opt to optimize the ACO parameter combination through Seagull Optimization Algorithm (SOA) to strengthen ACO's search capability. To obtain a uniform initial distribution of the ACO parameter combination, we incorporated the Kent Chaos Map (KCM) to randomly initialize the seagull's position, reducing the tendency of SOA to fall into the local optimum. To avoid slow calculation speed and premature convergence of ACO, we improved the adaptive multi-population mechanism to reduce repeated redundant calculations and used the ϵ − g r e e d y and default strategy, respectively, to update the ants' position. 2Opt is applied to find shorter paths in each iteration. In addition, when AUV navigates on the planned path, it may encounter obstacles. Therefore, this paper proposes an autonomous obstacle avoidance algorithm based on forward-looking sonar to ensure safety during tasks. SOA-ACO-2Opt is verified against twelve different problems extracted from TSPLIB and compared with some state-of-the-art algorithms. Furthermore, sea trials were carried out for several representative marine engineering applications of TSP and obstacle avoidance. Experimental results show that this work can significantly improve AUV's work efficiency and intelligence and protect the AUV's safety. [Display omitted] • A hybrid algorithm SOA-ACO-2Opt is proposed to solve TSP. • A multi-population mechanism is used to reduce redundant computation and avoid early convergence. • The desired-path-based avoidance strategy is efficient and of low complexity. • The proposed algorithm is practical and efficient for AUV engineering applications. [ABSTRACT FROM AUTHOR]

Path planning is a key factor for the unmanned surface vehicle (USV) to achieve efficient navigation. In this paper, to solve the global path planning and obstacle avoidance problems for the USV, an improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed. Three main improvement parts are composed of the proposed algorithm. Firstly, the radial basis function (RBF) neural network is combined with the Q-Learning algorithm to approximate the action value function Q, which improves the convergence speed of the Q-Learning algorithm. Secondly, to ensure that the planned path conforms to the maneuvering characteristics of the USV, the heading angle, motion characteristics, ship length, and safety of the USV are taken into account by the proposed algorithm. Based on these factors, the action space and reward function are optimized, the state space is reconstructed, and the safety threshold is proposed. Finally, a third-order Bezier curve is used to smooth the initial path, so that the USV can maintain its heading stability during navigation. Based on simulation results, the proposed NSFQ algorithm outperforms the A* and RRT algorithms in terms of evaluation indicators such as heading angle, angular velocity, path length, sailing time, and path smoothness. • An improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed in this paper. • The heading angle, angular velocity, and other indicators of the USV are taken into account by this proposed algorithm. • A safety threshold is proposed to ensure the safety of the USV. • A third-order Bezier curve is used to smooth the initial path. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments. • A Q-learning based multi-strategy integrated ABC algorithm is proposed. • Multiple strategies are introduced to the onlooker bee phase. • Q-learning is utilized to adaptively determine the most suitable strategy. • Novel state and action settings are designed in Q-learning framework. • Proposed algorithm shows superiority in numerical and practical experiments. [ABSTRACT FROM AUTHOR]