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Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AI-based AV systems. [ABSTRACT FROM AUTHOR]

Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large uniform regions, and occlusions. Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted a growing interest from the community, with more than 150 papers published in this area between 2014 and 2019. This new generation of methods has demonstrated a significant leap in performance, enabling applications such as autonomous driving and augmented reality. In this paper, we provide a comprehensive survey of this new and continuously growing field of research, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for deep learning-based stereo for depth estimation research. [ABSTRACT FROM AUTHOR]

The technical maturity of autonomous driving enables the discussion of beneficial use cases to leverage its full potential. In this paper, we target one such use case: Platooning is the efficient convoying of vehicles by making use of self-driving capabilities and inter-vehicle communication. Many advantages arise from grouping vehicles in platoons with a small inter-vehicle distance, such as energy savings, congestion reduction, and safety improvements. However, due to the diversity of involved stakeholders, numerous objectives have to be balanced to leverage the full potential of platooning. Furthermore, these objectives also depend on various factors that influence their optimization. The vast majority of existent literature only targets a subset of related objectives and underlying factors. This paper provides an overview which categorizes objectives and influencing factors. Additionally, metrics for the evaluation of objective attainment are proposed. [ABSTRACT FROM AUTHOR]

In the recent past, platooning evolved into an attractive cooperative driving technology, broadly discussed in research and practice. Vehicles in platoons use cooperative adaptive cruise control to drive at close distances to each other. Platooning (i) increases the capacity of the street by a factor of 2; (ii) reduces the fuel consumption and emissions by up to 20%; and (iii) has social implications as it increases driver comfort and safety. As platooning research progresses, platooning coordination becomes a major research focus. The coordination of platoons, including the assignment of vehicles to platoons, the management of inter- and intra-platoon interactions, and the coordination of interactions with other vehicles is an important step towards an effective usage of platooning in practice. Based on a literature review of 1,600 papers, this survey provides an overview of state of the art in platooning coordination research for both cars and trucks. In this paper, we present a novel taxonomy for platooning coordination and classify existing approaches. We use the results of the literature review to discuss challenges and outline avenues for future work such as multi-objectiveness and individualisation. [ABSTRACT FROM AUTHOR]

In this paper, a new framework for path tracking is proposed through learning to drive like human beings. Firstly, the imitation algorithm (behavior cloning) is adopted to initialize the deep reinforcement learning (DRL) algorithm through learning the professional drivers’ experience. Secondly, a continuous, deterministic, model free deep reinforcement learning algorithm is adopted to optimize our DRL model on line through trial and error. By combining behavior cloning and deep reinforcement learning algorithms, the DRL model can learn an effective policy quickly for path tracking using some easy-to-measure vehicle state parameters and environment information as inputs. Actor-Critic structure is adopted in the DRL algorithm. In order to speed up the convergence rate of the DRL model and improve the learning effect, we propose a dual actor networks structure for the two different action outputs (steering wheel angle and vehicle speed), and a chief critic network is built to guide the updating process of dual actor networks at the same time. Based on this dual actor networks structure, we can pick out some more important state information as state inputs for different action outputs. Besides, a kind of reward mechanism is also presented for autonomous driving. Finally, simulation training and experiment test are carried out, and the results confirm that the framework proposed in this paper is more than data efficient than the original algorithm, and the trained DRL model can track the reference path with accuracy and has the generalization ability for different roads. [ABSTRACT FROM AUTHOR]

This paper aims to resolve a clear and unrealistic limitation found in recent works that have proposed solutions to the massive demand for the autonomous electric mobility-on-demand (AEMoD) services. Namely, this paper will focus on the matched charge-to-trip only service by enabling sub-class service; i.e., allowing vehicles to serve customer classes with trips needing less charge. The paper proposes a multi-class management system with a new queuing model for one city zone, and then derives the stability conditions of the system. Proportions of vehicles from each class that will immediately serve customers with sub-class service or full/partial charge are modeled, and the maximum expected response time of the system is then minimized by optimizing these vehicle service decisions. The advantages of the new model are then compared with previously proposed schemes and non-optimized models through simulations. [ABSTRACT FROM AUTHOR]

This paper addresses a UAV path planning problem for a team of cooperating heterogeneous vehicles composed of one unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs). The UGVs are used as mobile actuators and scattered in a large area. To achieve multi-UGV communication and collaboration, the UAV, modeled as a Dubins vehicle, serves as a messenger to fly over the effective communication range of all UGVs to relay information. The curvature-constrained path planning of the messenger UAV is formulated as a Dubins Traveling Salesman Problem with Dynamic Neighborhood (DTSPDN) which is a complex optimization problem involving coupled variables and contains dynamic constraints. We design an effective memetic algorithm to find the shortest route that enables the messenger UAV to visit all moving UGVs. This algorithm combines the genetic algorithm procedure, two kinds of local search operators based on gradient search and uniform sampling respectively, and a gradient-based repair operator to repair the solutions violating dynamic constraints. During the evolutionary process, a special phenomenon may occur that changing some decision variables (i.e., visiting sequence and location) may not affect the evaluation function value, but may alter the feasible region of another decision variable (i.e., visiting time) due to the encounter constraint between the UAV and UGV. To track and utilize the change of the feasible region, a transformation procedure is proposed to change one solution to another with less visiting time by analyzing the encounter pattern between UAV and UGV. The computational results on random instances with different scales demonstrate that the proposed approach can effectively generate better curvature-constrained tours to encounter all moving UGVs when compared to other four competitive algorithms in the literature. Note to Practitioners—This paper studies an emerging path planning problem for a UAV which is used to provide communication service for multiple moving UGVs. These UGVs are required to execute tasks (e.g., firefighting, search and rescue) within a large area. Due to their limited communication capabilities, they may be unable to obtain necessary information from other UGVs. The UAV serves as a messenger to fly over the effective communication range of all moving UGVs to relay information. We propose a novel memetic algorithm to efficiently search for the shortest tour that enables the messenger UAV to visit all moving UGVs. The memetic algorithm combines the parallel global search virtue of genetic algorithm with efficient local search procedure to improve the generated tour. A gradient-based repair procedure is also employed to make sure that the planned tour can guide the UAV to sequentially encounter each moving UGV. Simulations exhibit that the proposed approach can effectively generate high-quality tours for messenger UAV to rapidly visit all UGVs, which assists UGVs to achieve collaboration in large area. In future work, the proposed memetic algorithm will be extended to plan tours for multiple messenger UAVs. [ABSTRACT FROM AUTHOR]

Recently, new research activities have emerged for controlling traffic. Since future vehicles will travel autonomously and communicate with their surrounding environment, they will then be able to negotiate the right-of-way at intersections [cooperative intersection management (CIM)] as well as reserve their itinerary (road reservation). Both concepts, i.e., CIM and road reservation, are very promising for relieving traffic congestion. The scope of this paper is to prevent deadlock under real-time conditions, by taking advantage of these two concepts. This paper presents an appropriate graph to model the network of intersections and a sufficient condition for obtaining deadlock-free traffic with a specified route from their origin to their destination. In addition, it proposes a hierarchical approach in which a network server, intersection servers, and vehicles contribute to improving the traffic condition. Simulations were performed on a network of 25 interconnected intersections, as well as, on a real urban network to prove the effectiveness of the model. The results are presented and discussed. [ABSTRACT FROM AUTHOR]

End-to-end steering estimation is one of the important deep regression tasks. However, driving datasets are always imbalanced on the distribution of the steering value, which makes end-to-end learning models perform bad steering estimation accuracy, especially for the sharp steering value estimation, which finally, leads to an imbalanced training problem. In this paper, the essential reason for the imbalanced training problem for the deep regression task is first revealed as the gradient disharmony. To hedge the gradient disharmony, a general gradient balanced mechanism for the deep regression task is proposed in this paper. Meanwhile, the general gradient balanced mechanism is applied for the steering estimation task. According to the steering distribution, a novel Adaptive Gradient Balanced Mechanism (AGBM) is proposed to hedge the gradient disharmony based on the Laplace distribution with the general gradient balanced mechanism theory. Further, a new loss function embedding with AGBM is proposed to balance the gradient disharmony for the training process. AGBM makes the process of gradient hedging with adaptive style without parameters finetuning. Based on five driving datasets, four experiments are conducted for demonstrations. The experiment results show the AGBM enables the end-to-end models to perform the best accuracy of the steering estimation, including the sharp steering samples. Meanwhile, AGBM is generalized with different end-to-end learning models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new state representation method, called encoding sum and concatenation (ESC), to describe the environment observation for decision-making in autonomous driving. Unlike existing state representation methods, ESC is applicable to the situation where the number of surrounding vehicles is variable and eliminates the need for manually pre-designed sorting rules, leading to higher representation ability and generality. The proposed ESC method introduces a feature neural network (NN) to encode the real-valued feature of each surrounding vehicle into an encoding vector, and then adds these vectors up to obtain the representation vector of the set of surrounding vehicles. Then, a fixed-dimensional and permutation-invariance state representation can be obtained by concatenating the set representation with other variables, such as indicators of the ego vehicle and road. By introducing the sum-of-power mapping, this paper has further proved that the injectivity of the ESC state representation can be guaranteed if the output dimension of the feature NN is greater than the number of variables of all surrounding vehicles. This means that the ESC representation can be used to describe the environment and taken as the inputs of learning-based policy functions. Experiments demonstrate that compared with the fixed-permutation representation method, the policy learning accuracy based on ESC representation is improved by 62.2%. [ABSTRACT FROM AUTHOR]

Recent advances in smart connected vehicles and Intelligent Transportation Systems (ITS) are based upon the capture and processing of large amounts of sensor data. Modern vehicles contain many internal sensors to monitor a wide range of mechanical and electrical systems and the move to semi-autonomous vehicles adds outward looking sensors such as cameras, lidar, and radar. ITS is starting to connect existing sensors such as road cameras, traffic density sensors, traffic speed sensors, emergency vehicle, and public transport transponders. This disparate range of data is then processed to produce a fused situation awareness of the road network and used to provide real-time management, with much of the decision making automated. Road networks have quiet periods followed by peak traffic periods and cloud computing can provide a good solution for dealing with peaks by providing offloading of processing and scaling-up as required, but in some situations latency to traditional cloud data centres is too high or bandwidth is too constrained. Cloud computing at the edge of the network, close to the vehicle and ITS sensor, can provide a solution for latency and bandwidth constraints but the high mobility of vehicles and heterogeneity of infrastructure still needs to be addressed. This paper surveys the literature for cloud computing use with ITS and connected vehicles and provides taxonomies for that plus their use cases. We finish by identifying where further research is needed in order to enable vehicles and ITS to use edge cloud computing in a fully managed and automated way. We surveyed 496 papers covering a seven-year timespan with the first paper appearing in 2013 and ending at the conclusion of 2019. [ABSTRACT FROM AUTHOR]

Identifying and evaluating the potential risks in the surrounding environment is critical for intelligent vehicles’ safety and user experience. This paper provides a comprehensive overview of the state-of-the-art research on the surrounding vehicles’ lane change maneuver prediction and detection. First, various driver behavior modeling and classification methods are reviewed and analyzed, which gives a general understanding of what the lane change maneuver is and how to predict or detect the lane change maneuver. Next, the primary sensing devices equipped on intelligent vehicles and their impacts on lane change inference systems are discussed. Then, a series of representative research works in recent years are selected, introduced, and compared regarding their input feature selection, inference algorithms, and performance evaluation methods. Finally, some potential future research directions are proposed. This paper aims to help the relevant researchers and institutions summarize the current studies on the surrounding vehicles’ lane change maneuver inference and recognize its future development directions. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber-security problem for autonomous vehicles under sensor attacks. In particular, a model-based framework is proposed which can detect sensor attacks and identify their sources in order to achieve the secure localization of self-driving vehicles. To ensure robustness of the vehicle against cyber-attacks, sensor redundancy is introduced, that is to deploy multiple sensors, each of which provides real-time pose observations of the vehicle. A bank of attack detectors is developed to capture anomalies in each sensor measurement, which is a combination of an extended Kalman filter (EKF) and a cumulative sum (CUSUM) discriminator. EKFs are employed to estimate the vehicle position and orientation recursively, while each CUSUM discriminator is designed to analyze the residual generated by its combined EKF to detect the possible deviation of the sensor measurement from the expected pose derived according to the mathematical model of the vehicle. To monitor the inconsistency amongst multiple sensor measurements, an auxiliary detector is introduced which fuses observations from multiple sensors. Based on the results of all the detectors, a rule-based isolation scheme is developed to identify the source anomalous sensor. The effectiveness of our proposed framework has been demonstrated on real vehicle data. [ABSTRACT FROM AUTHOR]

Highway platooning of vehicles has been identified as a promising framework in developing intelligent transportation systems. By autonomous or semi-autonomous vehicle control and intervehicle coordination, an appropriately managed platoon can potentially offer enhanced safety, improved highway utility, increased fuel economy, and reduced emissions. This paper is focused on quantitative characterization of the impact of communication information structures and contents on platoon safety. By comparing different information structures that combine front sensors, rear sensors, and wireless communication channels, as well as different information contents such as distances, speeds, and drivers' actions, we reveal a number of intrinsic relationships between vehicle coordination and communications in platoons. Typical communication standards and related communication latency are used as benchmark cases in this paper. The findings of this paper provide useful guidelines in sensor selections, communication resource allocations, and vehicle coordination. [ABSTRACT FROM AUTHOR]

Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current frame using the previous frame by employing a novel feature space inter-prediction network. The proposed network utilizes sparse convolutions with hierarchical multiscale 3D feature learning to encode the current frame using the previous frame. The proposed method introduces a novel predictor network for motion compensation in the feature domain to map the latent representation of the previous frame to the coordinates of the current frame to predict the current frame’s feature embedding. The framework transmits the residual of the predicted features and the actual features by compressing them using a learned probabilistic factorized entropy model. At the receiver, the decoder hierarchically reconstructs the current frame by progressively rescaling the feature embedding. The proposed framework is compared to the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). The proposed method achieves more than 88% BD-Rate (Bjøntegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC. These significant performance gains are cross-checked and verified in the MPEG working group. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel visual-inertial navigation system with motion constraint (VINS-Motion), which extends the visual-inertial navigation system (VINS) to incorporate vehicle motion information for improving the autonomous vehicles localization accuracy. We introduce the vehicle stop detection module in the pre-processing measurement procedure. During the back-end processing, if the vehicle is detected to be in a stopping state, we perform optimization based on the stop constraint to help eliminate the abnormal jitter of the estimated pose, thus ensuring the reasonability of the trajectory. Otherwise, besides the prior information, IMU measurement residual, and visual measurement residual utilized in VINS, vehicle orientation/velocity constraint built by Ackerman steering model is first exploited to constitute residuals. We minimize the sum of priors and Mahalanobis norms of three kinds of residuals to obtain a maximum posterior estimation, thus increasing system consistency and accuracy. The proposed approach is validated on public datasets and compared with state-of-the-art algorithms, which demonstrates that the motion constraint proposed in this paper improves the standard VINS performance and achieves significantly higher positioning accuracy. [ABSTRACT FROM AUTHOR]

Traditional vehicle localization methods use information from a GPS, inertial navigation, and an odometer, but a GPS is often limited by availability in urban areas for its sensitivity to terrain and interference. Aiming at the above problems, this paper presents a method of intersection localization using a traffic lights’ map. In particular, traffic lights with significant visual characteristics in the urban environment are used as landmarks. The localization accuracy of the autonomous vehicle at intersections is improved by combining the location information of traffic lights provided by a high-precision map. The whole scheme of the localization system is designed and the sensors used are determined. First, the coordinates of sensors are established, respectively, and the transformation and rotation are calibrated. Then, the state model and measurement model of the vehicle vision system are established. Combined with the location and height information of the traffic lights provided by the high-precision map, the extended Kalman filter is used to fuse the vision detection results of the traffic lights with the inertial measurement unit information. The experiments demonstrate that the method proposed in this paper improves the lateral localization accuracy and the accuracy of the vehicle’s yaw angle. [ABSTRACT FROM AUTHOR]

Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle's automatic speed control strategy to make judgments and effective control decisions. In this paper, an intelligent speed control strategy for uncertain cut-in scenarios is established based on a basic autonomous driving system. This strategy judges cut-in maneuver from surrounding vehicles and outputs adaptive control action under current environment according to Q value of state-action pair based on a Q network. In addition, according to the analysis of cut-in scenarios, the Q network is trained based on a novel reinforcement learning method named as experience screening deep Q-learning network (ES-DQN). The proposed ES-DQN is an extension of double deep Q-learning network (DDQN) algorithm, and includes two parts: experience screening and policy learning. Based on the experience screened from the experience screening part, the proposed learning method can train an intelligent speed control strategy which has stronger adaptability and control effect in uncertain cut-in scenarios. According to simulation results, the proposed intelligent speed control strategy trained by ES-DQN has better performance under uncertain cut-in scenarios than DDQN method and traditional ACC strategy. Meanwhile, by adjusting weight value in reward function, the system can realize different control target. [ABSTRACT FROM AUTHOR]

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed. [ABSTRACT FROM AUTHOR]

The paper in this special section focus on technological breakthroughs in hardware structure, modeling mechanisms, innovative design methodologies for mechatronic systems, development of efficient algorithms, novel sensors and actuators, medical robotics and rehabilitation devices, soft robotics, bio-inspired robotic designs, learning methods in robotics and control, motion planning and control, as well as control of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Many autonomous vehicles and advanced driver-assistance systems are equipped with front-facing cameras that detect and track objects using deep-learning-based algorithms. However, the localization capability of monocular cameras is often overlooked. In this paper, a novel method for estimating the pixel-wise error in a detected object's location versus its ground truth is proposed. As the object moves away from the camera, the pixel errors are shown to be normally distributed with unique spreads along the image's vertical axis (y-pixel). The pixel error appears to be smaller as objects get farther away, while at the same distance range, objects have similar error distribution across the camera's horizontal view. The horizontal axis (x-pixel) error appears to be smaller while the distance moves further away. However, the x-pixel location along a constant y-pixel row has no impact on the error distribution. The estimated x and y-pixel error distributions can in turn be used to form a spatial error distribution for finding the location of a detected object within a certain confidence interval. The spatial errors are then projected onto the world coordinate system using a camera transformation matrix to give a more realistic sense of what this error means. The results show that location estimation using monocular cameras generates an elliptical error distribution around the object with a larger error in the y-pixel direction compared to the x-pixel direction. This error distribution can be important to fuse information from multiple range-detecting sensors as well as multi-vehicle and multi-object tracking. The uncertainty characterization for position measurement, as demonstrated in this paper is an essential element of tracking and, is sensor and algorithm dependent. [ABSTRACT FROM AUTHOR]

Recognizing wet surfaces and their degrees of wetness is essential for many computer vision applications. Surface wetness can inform us slippery spots on a road to autonomous vehicles, muddy areas of a trail to humanoid robots, and the freshness of groceries to us. The fact that surfaces darken when wet, i.e., monochromatic appearance change, has been modeled to recognize wet surfaces in the past. In this paper, we show that color change, particularly in its spectral behavior, carries rich information about surface wetness. We first derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface. We present a novel method for estimating key parameters of this spectral appearance model, which enables the recovery of the original surface color and the degree of wetness from a single multispectral image. Applied to a multispectral image, the method estimates the spatial map of wetness together with the dry spectral distribution of the surface. To our knowledge, this is the first work to model and leverage the spectral characteristics of wet surfaces to decipher its appearance. We conduct comprehensive experimental validation with a number of wet real surfaces. The results demonstrate the accuracy of our model and the effectiveness of our method for surface wetness and color estimation. [ABSTRACT FROM AUTHOR]

Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better safety for drivers and passengers. This paper proposes a distributed motion planning algorithm to ensure safe overtaking of autonomous vehicles in a dynamic environment using the Artificial Potential Field method. Unlike the conventional overtaking techniques, autonomous driving strategies can be used to implement safe overtaking via formation control of unmanned vehicles in a complex vehicle platoon in the presence of human-operated vehicles. Firstly, we formulate the overtaking problem of a group of autonomous vehicles into a multi-target tracking problem, where the targets are dynamic. To model a multi-vehicle system consisting of both autonomous and human-operated vehicles, we introduce the notion of velocity difference potential field and acceleration difference potential field. We then analyze the stability of the multi-lane vehicle platoon and propose an optimization-based algorithm for solving the overtaking problem by placing a dynamic target in the traditional artificial potential field. A simulation case study has been performed to verify the feasibility and effectiveness of the proposed distributed motion control strategy for safe overtaking in a multi-lane vehicle platoon. [ABSTRACT FROM AUTHOR]

Connected autonomous vehicle technology is conductive to promoting the transition from traditional merging control (e.g., ramp metering) to automated merging control. This paper proposes a platoon-based hierarchical merging control algorithm for on-ramp vehicles to achieve automated merging control under connected traffic environment. The proposed algorithm optimizes merging maneuvers of on-ramp vehicles to smooth their merging trajectories without frequent decelerations or stops at the end of the ramp, and to minimize disruption to the mainline traffic in the merging zone. A tactical layer controller is designed to select pre-target merging gaps for on-ramp vehicles, in which the future motion (i.e., acceleration and deceleration) of mainline vehicles is considered through the grey prediction model. An operational layer controller is constructed based on model predictive control to adjust the speed of on-ramp vehicles in advance, and controls on-ramp vehicles to merge into the pre-target merging gaps under state constraints (i.e., safe headway, maximum speed and so on). Through numerical simulation, the effectiveness of the proposed algorithm is validated under different merging scenarios. It is shown that on-ramp vehicles smoothly merge into the mainline within the pre-target merging gap at the same speed as adjacent mainline vehicles. Compared with the baseline merging control algorithm, the proposed algorithm significantly reduces both fuel consumption and travel time of on-ramp vehicles, and improves passenger comfort. [ABSTRACT FROM AUTHOR]

Autonomous Driving (AD) introduces dramatic changes to the way we travel. This emerging technology has the potential to impact the transportation sector across a wide array of categories including safety, efficiency, congestion, legislation, and travel behavior. In this survey, we review the main issues involved in AD as discussed in the literature, and shed light on topics that we consider requiring further development based on Google Scholar and the Web of Science (WoS) data base. The paper also provides the results of research trends related to Autonomous Driving based on analysis of the number of search results listed in google trends. According to our research, the fields of Vehicle-to-Vehicle (V2V) and Vehicle-to-Cloud (V2C) networking are of higher interest due to the technological gaps and standardization processes. In addition, cyber and security research is in acceleration due to its importance. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AVs) are a significant part of Vehicular Adhoc NETwork (VANET) as they increase transportation accessibility. However, the presence of unpredictably sized potholes on road surfaces hampers the comfort and safety of autonomous navigation. Existing pothole avoidance mechanisms cannot dynamically adapt in unpredictable environments and do not comfort the traveler well in VANET. This paper proposes a novel Smart Pothole-Avoidance Strategy (SPAS) for safe navigation in a pothole-intensive environment. Potholes are avoided using the Deep Deterministic Policy Gradient (DDPG) algorithm as it performs best in continuous action space tasks and has a faster convergence speed. A Hybrid Recognition Model using the Speech and Gesture mechanism (HRM-SG) is proposed in this paper to collect the traveler’s real-time audio and visual feedback for the DDPG reward function. The received feedback aids in fine-tuning the model to avoid the pothole efficiently than the previous pothole. Traveler’s feedback is coupled with the vehicle’s sensor data and used to decide the time, speed, and angle at which lane change and speed change are executed. Finally, the SPAS continuously optimizes lane change parameters in VANET to achieve maximal traveler comfort during the operation. The result analysis indicates that SPAS achieves a 10-15% improvement in the accuracy of pothole avoidance, 10-12% higher comfort, and 8-10% faster convergence than the existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Vehicle localization in highway tunnels is challenging for autonomous vehicle navigation. The drift error increases because GPS signals from satellites cannot be received inside a highway tunnel. Therefore, map-aided localization is essential for correcting the drift error. However, the environment around the tunnel is composed mainly of elliptical walls, and there are few unique feature points for map matching, unlike the case outdoors. In this paper, we propose a tunnel facility–based precise vehicle localization method for highway tunnels using 3D LIDAR. For vehicle localization, a point landmark map storing the center points of the tunnel facilities and a probability distribution map storing the probability distributions of the lane markings is used. In the proposed method, the tunnel facility points are extracted using 3D LIDAR, and position estimation is conducted using an extended Kalman filter-based navigation filter. The proposed localization algorithm was verified through experiments using actual highway driving data. The experimental results verify that tunnel facility–based vehicle localization yields precise results in various situations. [ABSTRACT FROM AUTHOR]

Delay and lag deteriorate path tracking accuracy and system stability. If not properly compensated, they can cause instability or limit the driving speed of autonomous vehicles. This paper presents a preview steering control design considering both communication delay and steering lag to achieve accurate, smooth, and computationally efficient path tracking for highly automated vehicles. Two strategies are adopted for the delay: forward state predictor and delay augmentation. The steering lag is approximated by a first-order lag system. The path tracking problem with delay and lag is solved by the preview control theory. The resulted controller is in an analytical form and is computationally efficient for online implementation. We also analyze the system stability and closed-loop responses in both the time and frequency domain. The control is implemented on an automated vehicle platform and tested inside Mcity and on open roads. Experimental results showed lower tracking errors and significantly improved stability margin compared to the controls ignoring delay and lag. [ABSTRACT FROM AUTHOR]

Pedestrian Detection is the most critical module of an Autonomous Driving system. Although a camera is commonly used for this purpose, its quality degrades severely in low-light night time driving scenarios. On the other hand, the quality of a thermal camera image remains unaffected in similar conditions. This paper proposes an end-to-end multimodal fusion model for pedestrian detection using RGB and thermal images. Its novel spatio-contextual deep network architecture is capable of exploiting the multimodal input efficiently. It consists of two distinct deformable ResNeXt-50 encoders for feature extraction from the two modalities. Fusion of these two encoded features takes place inside a multimodal feature embedding module (MuFEm) consisting of several groups of a pair of Graph Attention Network and a feature fusion unit. The output of the last feature fusion unit of MuFEm is subsequently passed to two CRFs for their spatial refinement. Further enhancement of the features is achieved by applying channel-wise attention and extraction of contextual information with the help of four RNNs traversing in four different directions. Finally, these feature maps are used by a single-stage decoder to generate the bounding box of each pedestrian and the score map. We have performed extensive experiments of the proposed framework on three publicly available multimodal pedestrian detection benchmark datasets, namely KAIST, CVC-14, and UTokyo. The results on each of them improved the respective state-of-the-art performance. A short video giving an overview of this work along with its qualitative results can be seen at https://youtu.be/FDJdSifuuCs. Our source code will be released upon publication of the paper. [ABSTRACT FROM AUTHOR]

Most existing studies on autonomous intersection management (AIM) often focus on algorithms to accommodate conflicts among vehicles by assuming that the entrance lane and the exit lane of vehicles are exogenous inputs. This paper shows that allowing entrance lanes and exit lanes to be optimized can significantly improve traffic efficiency. In particular, this paper proposes “all-direction” lanes, where left-turn, through, and right-turn traffic is all allowed at the same lane. We develop two methods for optimizing entering time (i.e., when to enter the intersection) and route choice decisions (i.e., entrance lane and exit lane), including the sliding-time-window-based global optimum (GO-STW) and the first-come-first-served method with optimal route choices (FCFS-R). The developed lane-based methods can be formulated as mixed integer linear programming (MILP) problems, which can be solved using the CPLEX solver. A heuristic is further adopted to solve the MILP model in a timely manner, which illustrates the potential real-time applicability of the proposed method. Numerical analysis is conducted to examine performance and effectiveness of the proposed methods and heuristic. We found that the optimization of lane/route choices is often more critical than entering time. [ABSTRACT FROM AUTHOR]

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling heavy-duty trucks traveling through urban/suburban environments, where future intersection signal phase and timing (SPaT) information is non-deterministic. Through vehicle-to-infrastructure communication, past SPaT information is sent to the vehicle and is used to forecast future predictions of the signal timing using a Gaussian Process (GP) model. The h-GLAS strategy uses the predicted SPaT information to generate an efficient desired velocity profile that navigates through intersections where the probability of a green light is maximized. This velocity profile is tracked by a convex model predictive controller (MPC) that simultaneously minimizes mechanical energy expenditure and braking effort over its prediction horizon. Downstream from the MPC, we implement a command governor (CG) that adjusts the MPC output by the minimum amount necessary to maintain safe vehicle following and ensure that the vehicle stops at all red lights. Using a proprietary medium-fidelity Simulink model provided by Volvo Trucks, we characterize the h-GLAS strategy’s performance over a real suburban route, consisting of 10 actuated signalized intersections, using actual SPaT information provided by the NC Department of Traffic (DOT). Simulation results demonstrate a 30-43% reduction in fuel consumption, as compared to a baseline control strategy, which is attributable primarily to avoiding the massive energy losses associated with braking. Furthermore, we evaluate the computational efficiency of our approach by assessing average simulation execution times. [ABSTRACT FROM AUTHOR]

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environment and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs’ behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have ‘seen’, done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations by highlighting the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their AV explanation requirements. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, vehicle control, and system management). Finally, we discuss pertinent challenges and provide recommendations including a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explanation provisions in autonomous driving. [ABSTRACT FROM AUTHOR]

The complex traffic and road environment pose considerable challenges to the accuracy, timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making driving decisions. This paper uses vehicle collaboration and integrates the adaptive learning capabilities of machine learning and the interpretation capabilities of expert systems (ESs) in a unified architecture to form a hybrid autonomous driving guidance system, which not only solves the “bottleneck” of knowledge acquisition during the construction of expert systems but also solves the “black box” phenomenon of machine learning in the decision-making process. First, an autonomous driving strategy based on deep reinforcement learning (DRL) is proposed for CAVs to make decisions and extract corresponding rules. Next, we design an ES knowledge base expansion method including rule extraction, rule sharing, and rule test. Particularly, vehicular blockchain is adopted to ensure user privacy and data security during the rule-sharing process. Third, hybrid autonomous driving guidance combining ES and machine learning is proposed for CAVs to make accurate and efficient decisions in different driving environments. Once the strategy is well trained, it can effectively guide CAVs to cope with the complex traffic environment. Extensive simulations validate the performance of our proposal in terms of decision-making accuracy, effectiveness, and safety. [ABSTRACT FROM AUTHOR]

This paper is about obtaining stable vehicle platooning by using Cooperative Adaptive Cruise Control when the communication is unreliable and suffers from message losses. We model communication losses as independent random events and we propose an original design for the cooperative controller, which effectively mitigates the effects of the losses. Our design explicitly takes into account the stochastic nature of the losses by considering both the average evolution of the system and the stochastic variations around it. The control design promotes both plant stability and string stability of the average error dynamics by an $\mathscr {H}_\infty $ approach, while minimizing the variance of the trajectories around their average. We show by simulations that the proposed controller is able to compensate losses even for high loss probabilities. [ABSTRACT FROM AUTHOR]

Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways. However, most existing evaluation approaches for autonomous vehicles are static and lack adaptability, so they are usually inefficient in generating challenging scenarios for tested vehicles. In this paper, we propose an adaptive evaluation framework to efficiently evaluate autonomous vehicles in adversarial environments generated by deep reinforcement learning. Considering the multimodal nature of dangerous scenarios, we use ensemble models to represent different local optimums for diversity. We then utilize a nonparametric Bayesian method to cluster the adversarial policies. The proposed method is validated in a typical lane-change scenario that involves frequent interactions between the ego vehicle and the surrounding vehicles. Results show that the adversarial scenarios generated by our method significantly degrade the performance of the tested vehicles. We also illustrate different patterns of generated adversarial environments, which can be used to infer the weaknesses of the tested vehicles. [ABSTRACT FROM AUTHOR]

The air-ground cooperative emergency networks can assist with the rapid reconstruction of communication in the disaster area, where unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are deployed as base stations. The trajectory optimization of emergency base stations is of vital importance to the communication performance, which is related to the timeliness and effectiveness of rescue. In this paper, federated multi-agent deep deterministic policy gradient (F-MADDPG) based trajectory optimization algorithm is proposed to maximize the average spectrum efficiency. Specifically, the property of MADDPG is inherited to jointly control of multiple vehicles and federated averaging (FA) is utilized to eliminate the isolation of data to accelerate the convergence. Distributed F-MADDPG (DF-MADDPG) is further designed to reduce the communication overhead with a distributed architecture. The simulation results indicate that the proposed F-MADDPG and DF-MADDPG based algorithms significantly outperform the existing trajectory optimization algorithms, in terms of the average spectrum efficiency and the speed of convergence. [ABSTRACT FROM AUTHOR]

The rapid advancement of sensor technologies and artificial intelligence are creating new opportunities for traffic safety enhancement. Dashboard cameras (dashcams) have been widely deployed on both human driving vehicles and automated driving vehicles. A computational intelligence model that can accurately and promptly predict accidents from the dashcam video will enhance the preparedness for accident prevention. The spatial-temporal interaction of traffic agents is complex. Visual cues for predicting a future accident are embedded deeply in dashcam video data. Therefore, the early anticipation of traffic accidents remains a challenge. Inspired by the attention behavior of humans in visually perceiving accident risks, this paper proposes a Dynamic Spatial-Temporal Attention (DSTA) network for the early accident anticipation from dashcam videos. The DSTA-network learns to select discriminative temporal segments of a video sequence with a Dynamic Temporal Attention (DTA) module. It also learns to focus on the informative spatial regions of frames with a Dynamic Spatial Attention (DSA) module. A Gated Recurrent Unit (GRU) is trained jointly with the attention modules to predict the probability of a future accident. The evaluation of the DSTA-network on two benchmark datasets confirms that it has exceeded the state-of-the-art performance. A thorough ablation study that assesses the DSTA-network at the component level reveals how the network achieves such performance. Furthermore, this paper proposes a method to fuse the prediction scores from two complementary models and verifies its effectiveness in further boosting the performance of early accident anticipation. [ABSTRACT FROM AUTHOR]

Deep learning (DL) brings autonomous vehicles (AVs) close to reality. However, the witness of many safety issues has raised a big concern about the reliability of AVs. To solve this problem, much research has been done to test deep learning-driven AVs. Generally, once a test input is produced, a developer needs to manually check its expected output. However, there often exists massive unlabeled test data (e.g., raw context traces in the real world). It is impractical to manually label all test inputs. Despite some works on automatic generation of test oracles, they are either task-specific or constrained to synthetic inputs. In this paper, we present a general and extensible framework, DeepSuite, to mitigate the manual effort of generating test oracles. The intuition behind is that not all test inputs are equally worth labelling. With limited testing budget, it is desirable to label a test suite with high diversity and a reasonable size. Due to the large search space, to optimize such test suites is of great challenge. To address it, DeepSuite employs a three-phase optimization method (i.e., selection, crossover, and mutation) to iteratively select representative but non-redundant test suites. Such conflicting profit/cost objectives are attained through a genetic algorithm with a well-defined multi-objective fitness function. In the experiments, we first show that the diversity of tests can be revealed by test criteria. Then, experiments on three widely-used datasets demonstrated the effectiveness of DeepSuite in generating test suites with competitive testing coverage and 68.42% smaller size, which greatly improves the data collection efficiency of testing DL-driven autonomous vehicles. [ABSTRACT FROM AUTHOR]

The rapid development of 6G can help to bring autonomous driving closed to the reality. Drivers and passengers will have more time for work and leisure spending in the vehicles, further generating a lot of data requirements. However, edge resources from small base stations are insufficient to match the wide variety of services of the future vehicular networks. Besides, due to the high-speed nature of the vehicles, users have to switch the connections among different base stations, whereas such way will cause external latency during the data request. Therefore, it is vital to enable the local cache of vehicle users to realize the reliable autonomous driving. In this paper, we consider caching the contents in the local cache, small base station, and edge server. In practice, the request preference of some single users may be different from a whole region. To maximize the efficiency of content cache, we design a strategy that uses reinforcement learning algorithm to optimize cache schemes on different devices. The experimental results demonstrate that our strategy can enhance the cache hit ratio by 10%-20% compared with the well-known counterparts. [ABSTRACT FROM AUTHOR]

The growth of autonomous vehicles, ridesharing systems, and self-driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road congestion, environmental concerns, fuel usage, vehicles emissions, and the high cost of the vehicle usage have brought more attention to better utilize the use of vehicles and their capacities. In this paper, we propose a novel distributed multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions by interacting with the external environment. By allowing customers to transfer between vehicles, i.e., ride with one vehicle for some time and then transfer to another one, MHRS helps in attaining 30% lower cost and 20% more efficient utilization of fleets, as compared to the ride-sharing algorithms. This flexibility of multi-hop feature gives a seamless experience to customers and ride-sharing companies, and thus improves ride-sharing services. [ABSTRACT FROM AUTHOR]

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically. [ABSTRACT FROM AUTHOR]

Reinforcement learning has gradually demonstrated its decision-making ability in autonomous driving. Reinforcement learning is learning how to map states to actions by interacting with environment so as to maximize the long-term reward. Within limited interactions, the learner will get a suitable driving policy according to the designed reward function. However there will be a lot of unsafe behaviors during training in traditional reinforcement learning. This paper proposes a RL-based method combined with RL agent and Monte Carlo tree search algorithm to reduce unsafe behaviors. The proposed safe reinforcement learning framework mainly consists of two modules: risk state estimation module and safe policy search module. Once the future state will be risky calculated by the risk state estimation module using current state information and the action outputted by the RL agent, the MCTS based safe policy search module will activate to guarantee a safer exploration by adding an additional reward for risk actions. We test the approach in several random overtake scenarios, resulting in faster convergence and safer behaviors compared to traditional reinforcement learning. [ABSTRACT FROM AUTHOR]

Interpretation of common-yet-challenging inter- action scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. A discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway discretionary lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated discretionary lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View associated demos via: https://chengyuan-zhang.github.io/Multivehicle-Interaction . [ABSTRACT FROM AUTHOR]

Contemporary and emergent automotive systems are heavily populated by complex integrated electronics. The number of safety-critical devices used in advanced driver-assistance systems or autonomous vehicles is growing with high-end models containing hundreds of embedded microcontrollers. Achieving functionally safe automotive electronics requires test solutions that might be costly to engineer. Therefore, to address challenges posed by high-quality and long-term reliability requirements, this article presents low-cost test pattern generation schemes for a scan-based hybrid logic BIST of automotive ICs. It may allow one to optimize test coverage and test time during in-system test applications. The first presented technique deploys a seed-flipping PRPG to periodically complement PRPG stages in a methodical tree-traversal manner. The second scheme is based on a seed-sorting approach that allows additional tradeoffs between test data volume and test coverage. As shown in this article, the proposed schemes can be easily integrated with a test compression environment and deployed in different modes of in-system testing, such as key-off, key-on, and periodic (incremental) online tests. Experimental results obtained for automotive designs and reported herein show improvements in test quality over conventional logic BIST schemes. [ABSTRACT FROM AUTHOR]

Safety and efficiency are important priorities in transportation engineering. The new research directions in intelligent transportation systems focus on autonomy and collaboration to improve safety and efficiency. In this paper, we consider the problem of conflict resolution at an unsignalized intersection. Collision detection and avoidance are addressed based on an optimal strategy that uses collaboration between the vehicles. Lagrangian function is used to obtain closed-form solutions. This approach allows analysis of the collaboration process in terms of the cost and the time to resolve conflicts. Several approaches are used for weight assignments including reciprocal and unilateral actions. In this paper, reciprocal collaboration is defined as collaboration with equal costs. It is proven that the minimum cost to resolve any conflict corresponds to equal weights but not to reciprocal collaboration. On the other hand, reciprocal collaboration results in the minimum time to resolve a conflict. The proposed methods present effective solutions to the problem. Results are validated through simulation. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are expected to significantly enhance the human mobility. However, recently researchers have discovered and demonstrated some attacks on vehicles, which have caused a panic among the public. Furthermore, these attacks have demonstrated that the security issue is still one of the major challenges of vehicles. In this paper, we propose a novel edge computing based anomaly detection, coined edge computing based vehicle anomaly detection (EVAD), which exploits edge based sensor data fusion to identify the anomaly events. The time domain property, i.e., the correlation between different intra-vehicle sensors, and the frequency domain property of sensor data are utilized to judge whether an anomaly has occurred within the vehicle. Especially, to reduce the computation overhead and improve the performance, multiple sensors will be organized as ring architecture, which is a tradeoff of detection accuracy and complexity. In addition, the major components (e.g., anomaly detection module) of EVAD are embedded in edge computing devices, which make the anomaly detection be more efficient and privacy preserving. Meanwhile, a more appropriate model is generated on the cloud server, of which computation overhead maybe heavy for edge computing devices. This paper evaluates the performance of EVAD under different scenarios, and the experimental results demonstrate its feasibility and efficiency. The average true positive rate achieves 99.5% with 1% false positive rate. [ABSTRACT FROM AUTHOR]