IET Intelligent Transport Systems最新文献

Aiming at the power supply scheme (PSS) of the on-board supercapacitor-powered tram, considering the cost and margin of the PSS, a two-stage method is designed to optimize the layout of the charging stations and the configuration of the supercapacitor (SC). First, the SC-powered tram model and stable cycle operation model are established, and a two-stage optimization problem model with the lowest PSS cost and the largest SC margin is established. Then, an improved dual-population differential evolution algorithm is designed, and the layout of charging stations and the configuration of SC are co-optimized in the first stage, and then the layout of charging stations is optimized again in the second stage. The simulation results show that co-optimization can obtain a lower cost of PSS, and furthermore, the layout of charging stations can be optimized again to effectively improve the margin of SC, thereby improving the matching degree between the layout of charging stations and the connection scheme of SC.

Crashes between cars and powered two-wheelers (PTWs: motorcycles, scooters, and e-bikes) are a safety concern; as a result, developing car safety systems that protect PTW riders is essential. While the pre-crash protection system automated emergency braking (AEB) has been shown to avoid and mitigate injuries for car-to-car, car-to-cyclist, and car-to-pedestrian crashes, much is still unknown about its effectiveness in car-to-PTW crashes. Further, the characteristics of the crashes that remain after the introduction of such systems in traffic are also largely unknown. This study estimates the crash avoidance and injury risk reduction performance of six different PTW-AEB algorithms that were virtually applied to reconstructed car-to-PTW pre-crash kinematics extracted from a Chinese in-depth crash database. Five of the algorithms include combinations of drivers’ and PTW riders’ comfort zone boundaries for braking and steering, while the sixth is a traditional AEB. Results show that the average safety performance of the algorithms using only the driver's comfort zone boundaries is higher than that of the traditional AEB algorithm. All algorithms resulted in similar distributions of impact speed and impact locations, which means that in-crash protection systems likely can be made less complex, not having to consider differences in AEB algorithm design among car manufacturers.

The fundamental diagram (FD) of traffic flow can effectively characterize the macroscopic characteristics of traffic flow and provide a theoretical foundation for traffic planning and control. The rapid development of connected vehicles (CVs) has led to changes in traffic flow characteristics. However, research on the FD of traffic flow involving CVs and non-connected vehicles (NCVs) is still in its early stages. Most FDs do not well characterize the motion behaviour of different vehicles, nor do they study the interaction between mixed vehicles. Therefore, in this study, the FD of mixed traffic flows (i.e. with CVs and NCVs) was constructed within a unified framework. First, the car-following behaviours of CVs and NCVs were modelled based on risk potential field theory. Subsequently, the FD of mixed traffic flows was derived based on the relationship between car-following behaviour and the macroscopic traffic flow under steady-state conditions. To validate the model, rigorous verifications were conducted via numerical experiments using the Monte Carlo method. The results indicate significant agreement between the scatter plots obtained from the experiments and the theoretical curves for different penetration rates. The proposed FD has a unified framework and a more rigorous mathematical structure.

With the development of artificial intelligence, the application of unmanned ground vehicles (UGV) in outdoor hazardous scenarios has received more attention. However, the terrains in these environments are often complex and undulating, which also pose higher challenges to the multi-UGV path planning and task assignment (MUPPTA) optimization. To efficiently improve the multi-UGV collaboration in 3D environments, a MUPPTA method is proposed based on double deep Q learning network (DDQN) and ant colony optimization (ACO) to jointly optimize the path planning and task assignment decisions of multiple UGVs. The authors first comprehensively consider the characteristics of the 3D environments, and model the MUPPTA problem as a combinatorial optimization problem. To tackle it, the original problem is decomposed into the multi-UGV path planning sub-problem and task assignment sub-problem, and solve them separately. First, the path planning sub-problem in the 3D environments is transformed into a Markov decision process (MDP) model, and a multi-UGV path planning algorithm based on DDQN (MUPP-DDQN) is proposed to obtain the optimal paths and actual path costs between tasks through extensive offline learning and training. Based on this, a multi-UGV task assignment algorithm is further proposed based on ACO (MUTA-ACO) to solve the task assignment sub-problem and achieve the optimal task assignment solution. Simulation results show that the proposed method is more cost-effective and time-saving compared to other comparison algorithms.

In the cooperative vehicle-infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing-intensive delay-sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles’ tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy-packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions.

The purpose of signal control is to allocate time for competing traffic flows to ensure safety. Artificial intelligence has made transportation researchers more interested in adaptive traffic signal control, and recent literature confirms that deep reinforcement learning (DRL) can be effectively applied to adaptive traffic signal control. Deep neural networks enhance the learning potential of reinforcement learning. This study applies the DRL method, Double Deep Q-Network, to train local agents. Each local agent learns independently to accommodate the regional traffic flows and dynamics. After completing the learning, a global agent is created to integrate and unify the action policies selected by each local agent to achieve the purpose of traffic signal coordination. Traffic flow conditions are simulated through the simulation of urban mobility. The benefits of the proposed approach include improving the efficiency of intersections and minimizing the overall average waiting time of vehicles. The proposed multi-agent reinforcement learning model significantly improves the average vehicle waiting time and queue length compared with the results from PASSER-V and pre-timed signal setting strategies.

This study addresses the challenge of quantitatively assessing ride comfort in tram travel in Growing Urban Environments, where multiple influencing factors complicate developing a unified evaluation index system. A comprehensive evaluation framework based on cloud theory is proposed to overcome this challenge. The approach involves defining five-level comfort evaluation grades to capture passengers' experiences and perceptions accurately. The Criteria Importance through Inter-Criteria Correlation (CRITIC) method is employed to ensure objectivity to establish objective weights for evaluation indices. Subsequently, a cloud model algorithm is utilized to generate evaluation benchmark and actual result clouds, providing intuitive representations of the evaluation outcomes. The efficacy and rationality of the methodology is illustrated through a case study focusing on Suzhou Tram Line 2. This research contributes valuable insights for enhancing public transportation experiences in new urban settings by offering a systematic and objective approach to assessing tram ride comfort.

Connected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single-layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic.

Four-dimensional trajectory prediction is a crucial component of air traffic management, and its accuracy is closely related to the efficiency and safety of air transportation. Although long short-term memory (LSTM) or its variants have been widely used in recent studies, they may produce unacceptable results in long-term prediction due to the iterative output that accumulates error. To address this issue, a transformer-based long-term trajectory prediction model is proposed here, which utilizes the self-attention mechanism to extract time series features from historical trajectory data. For long-term prediction scenarios, we a trajectory stabilization module is introduced to ensure the stationarity of the time series for better predictability. Additionally, the transformer output strategy is improved to generate the prediction sequence by a single step instead of serial dynamic decoding, thus effectively enhancing the precision and inference speed. The proposed model is validated using real data obtained from China's Southwest Air Traffic Management Bureau. The experimental results demonstrate that this model outperforms the benchmark model. Further ablation experiments and visualizations are performed to analyze the impact of trajectory stabilization and one-step inference strategy.

A lightweight, high-definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, this paper presents an open-source and generic HDVM generation pipeline that integrates the global navigation satellite system (GNSS), inertial navigation system (INS), light detection and ranging (LiDAR), and camera. The pipeline begins by extracting semantic information from raw images using the Swin Transformer. The absolute 3D information of semantic objects is then retrieved using depth from the 3D LiDAR, and pose estimation from GNSS/INS integrated navigation system. Vector information (VI), such as lane lines, is extracted from the semantic information to construct the HDVM. To assess the potential error of the HDVM, this paper systematically quantifies the impacts of two key error sources, segmentation and LiDAR-camera extrinsic parameter error. An error propagation scheme is first formed to illustrate how these errors fundamentally influence the accuracy of the HDVM. The effectiveness of the proposed pipeline is demonstrated through our codeavailable at https://github.com/ebhrz/HDMap. The performance is verified using typical datasets, including indoor garages and complex urban scenarios.