Automated agents’ errors will cause various negative influences on humans and their relationships with humans (e.g., reducing user experience). They are increasingly required to have social recovery strategies (e.g., human-like apology and explanation) to mitigate the negative impacts of their errors and maintain resilient human–automation relationships. However, the efficacy of these strategies in human–automation interaction (HAI) largely remains unknown, especially in less controlled environments. Here we conducted a test track experiment and designed a verbal recovery design (consisting of an apology, explanation, and promise) by an automated driving system (ADS) installed in a real automated vehicle after an ADS failure. We utilized a Wizard of Oz design to simulate the ADS’ failure and its verbal recovery attempt (through a voice by a human or Apple Siri). Participants (N = 389) were assigned to four groups: normal (without experiencing the ADS failure), fault (experiencing the ADS failure), Siri-voice-recovery, and human-voice-recovery. The major measures were positive experience and negative experience while riding in the automated vehicle and perceived ADS usability. Overall, we found that the human-voice-recovery can to some degree mitigate the negative impacts of the ADS failure on user experience. The Siri-voice-recovery worked on positive experience but cannot restore it to that in the normal group. It implies that more empirical efforts are needed to examine social recovery strategies in HAI in natural environments, develop strategies specific to HAI, and offer effective guidelines for social recovery design.

Existing Unsupervised Domain Adaptation (UDA) methods often fall short in fully leveraging contextual information from the target domain, leading to suboptimal decision boundary separation during source and target domain alignment. To address this, we introduce GrabDAE, an innovative UDA framework designed to tackle domain shift in visual classification tasks. GrabDAE incorporates two key innovations: the Grab-Mask module, which blurs background information in target domain images, enabling the model to focus on essential, domain-relevant features through contrastive learning; and the Denoising Auto-Encoder (DAE), which enhances feature alignment by reconstructing features and filtering noise, ensuring a more robust adaptation to the target domain. These components empower GrabDAE to effectively handle unlabeled target domain data, significantly improving both classification accuracy and robustness. Extensive experiments on benchmark datasets, including VisDA-2017, Office-Home, and Office31, demonstrate that GrabDAE consistently surpasses state-of-the-art UDA methods, setting new performance benchmarks. By tackling UDA's critical challenges with its novel feature masking and denoising approach, GrabDAE offers both significant theoretical and practical advancements in domain adaptation.

Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angles leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world. To promote the Vehicle Re-ID models, this paper proposes to synthesize a large number of vehicle images in the target pose, whose idea is to project the vehicles of diverse poses into the unified target pose so as to enhance feature discrimination. Considering that the paired data of the same vehicles in different traffic surveillance cameras might be not available in the real world, we propose the first Pair-flexible Pose Guided Image Synthesis method for Vehicle Re-ID, named as VehicleGAN in this paper, which works for both supervised and unsupervised settings without the knowledge of geometric 3D models. Because of the feature distribution difference between real and synthetic data, simply training a traditional metric learning based Re-ID model with data-level fusion (i.e., data augmentation) is not satisfactory, therefore we propose a new Joint Metric Learning (JML) via effective feature-level fusion from both real and synthetic data. Intensive experimental results on the public VeRi-776 and VehicleID datasets prove the accuracy and effectiveness of our proposed VehicleGAN and JML.

Failures of the automated driving system (ADS) in automated vehicles (AVs) can damage driver–ADS cooperation (e.g., causing trust damage) and traffic safety. Researchers suggest infusing a human-like ability, active trust repair, into automated systems, to mitigate broken trust and other negative impacts resulting from their failures. Trust repair is regarded as a key ergonomic design in automated systems. Trust repair strategies (e.g., apology) are examined and supported by some evidence in controlled environments, however, rarely subjected to empirical evaluations in more naturalistic environments. To fill this gap, we conducted a test track study, invited participants (N= 257) to experience an ADS failure, and tested the influence of the ADS’ trust repair on trust and other psychological responses. Half of participants (n= 128) received the ADS’ verbal message (consisting of apology, explanation, and promise) by a human voice (n= 63) or by Apple's Siri (n= 65) after its failure. We measured seven psychological responses to AVs and ADS [e.g., trust and behavioral intention (BI)]. We found that both strategies cannot repair damaged trust. The human-voice-repair strategy can to some degree mitigate other detrimental influences (e.g., reductions in BI) resulting from the ADS failure, but this effect is only notable among participants without substantial driving experience. It points to the importance of conducting ecologically valid and field studies for validating human-like trust repair strategies in human–automation interaction and of developing trust repair strategies specific to safety-critical situations.

Connected and Automated Vehicles (CAV) platoon is regarded as a promising means of improving traffic efficiency and safety. This study focuses on addressing a Multi-Vehicle Motion Planning (MVMP) problem for CAVs aiming to form a platoon in the mixed traffic flow with both CAVs and Human-Driven Vehicles (HDV), which utilizes the cooperative capabilities of the multi-vehicle queue. Generally, the MVMP problem would be formulated into a centralized form, which is numerically intractable due to the computational complexity. In addition, the uncertainty of human drivers’ behavior in mixed traffic presents a challenge in motion planning for multiple CAVs simultaneously. To overcome these issues, we propose a decentralized MVMP framework based on the Monte Carlo Tree Search (MCTS) algorithm, which splits the MVMP problem into a series of lane change tasks. The MCTS algorithm is applied to determine an optimal lane change decision, facilitating the advancement of platoon formation based on the current vehicle state. Subsequently, we establish both the longitudinal position adjustment model and the lane change motion planning model to efficiently execute the lane change maneuver, as well as taking into account some evaluation factors such as safety, rapidity, and comfort. Finally, we develop a simulation platform using SUMO and MATLAB to simulate a three-lane freeway with mixed traffic. The simulation results demonstrate that the proposed approach can efficiently organize individual CAVs in the three lanes into a platoon under 20 scenarios including multiple traffic demands and CAV ratios. Furthermore, compared to the existing methods, the proposed approach achieves a better performance in terms of platooning proportion, time consumption, and time delay.

This paper proposed a speed harmonization controller for partially connected and automated traffic. It regulates the flow rate of the entire traffic by adjusting only the target cruising speed of Connected and Automated Vehicles (CAVs). The proposed controller bears the following features: i) compatibility enabled with partially connected and automated traffic consisting of CAVs and Human-driven Vehicles (HVs); ii) stability ensured for the traffic system under control; iii) precision guaranteed for the demand management on a multi-lane road with the help of a small portion of vehicles. To evaluate the proposed controller, a microscopic simulation evaluation was conducted. Results confirm that the control accuracy of the proposed controller is generally over 80% across all CAV Penetration Rates, demand levels (v/c ratio) and target demand drops (within 20%). A case study is presented to demonstrate the benefit of applying the proposed controller on a bottleneck. By preventing the onset of a breakdown and, along with it, a capacity drop, the proposed controller is able to increase the flow rate by 6%, reduce the number of stops by up to 90% and delay by approximately 5%.

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

Traffic incidents are recognized as a key contributor to non-recurrent congestion, which causes many negative effects in economy, environment, health and lifestyle. In this article, we investigate an incident management policy considering both signal control and route choice, which presents a real-time systematic effort to provide a rapid recovery from an incident and mitigate incident-related congestion according to different incident effects. Firstly, we introduce a route choice method on a multiple-route urban road network with consideration of bottleneck delays. Then, we analyze the route travel costs under incident effects and give the equilibrium existence condition after the occurrence of an incident. Furthermore, combining with the route choice method, a novel traffic signal control policy is proposed and the condition for equilibrium existence is given with the consideration of dynamic signal control and route choice simultaneously. Sufficient conditions for the dynamic road system to be stable are also derived and validated by using Lyapunov stability theorem. The analytical results indicate that opposite signal control policies should be applied in road networks under different incident circumstances and the proposed control policy can achieve the improved recovery rate and system stability than existing control policies in terms of dynamic incident effects in road networks. Finally, numerical results have been conducted to demonstrate the effectiveness of our proposed incident control policy and confirm the conditions for road system stability when different incident circumstances had been identified.

Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet been quantified. This paper compares the performance between fast heuristics and exact optimization models. We investigate a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given. To apply the SH algorithm to this problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model to solve the trajectory smoothing problems with different arrival and departure velocities. An exact trajectory optimization (ETO) model is formulated that takes the vehicle position and velocity as the decision variables, and the fuel consumption and driving comfort as the objective function. The constraints of the model are based on the limits and safety of the vehicle dynamics between consecutive vehicles. We demonstrate the convexity of the ETO objective function, ensuring the solvability of the ETO model at the true optimum using gradient descent algorithms supplied by the MATLAB optimization toolbox. Six groups of numerical experiments using different input parameters and one experiment using real Next Generation Simulation (NGSIM) data are conducted. ETO can improve the objective values by a few to tens of percentage points. However, FSSH achieves a greater solution efficiency with an average solution time of less than 0.1 s compared to ~450 s for ETO.

When facing with sudden terrorist attacks or natural disasters, in order to avoid the paralysis of communication networks caused by the destruction of partial ordinary 4G cellular base stations in urban area, this paper proposed a unmanned aerial vehicle (UAV) communication system based on 4G technology for public safety and investigated its optimal hovering height for maximizing its effective coverage radius. In this system, collaborative operation, among several airborne 4G cellular base stations, some unmanned aerial vehicle relays and other still worked ordinary 4G cellular base stations, could form a seamless communication coverage in the accident area, and provide the alternate communication links with QoS guarantee to people involved in the disaster relief and rescue. At meanwhile, in different urban environments, the hovering height of UAV equipped with the 4G cellular base station could be quickly optimal adjusted to maximize its effective coverage area by the configuration information sent from the emergency command management center, so as to effectively cut the cost of urban security emergency response system with the limited number of UAVs, ensure its smooth operation, and save people's life and property loss to the greatest extent.