Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Fei-Yue Wang;
Pages: 3763 - 3766 Abstract: Summary form only: Abstracts of articles presented in this issue of the publication. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Yilun Lin;Wei Hu;Xi Chen;Shuang Li;Fei-Yue Wang;
Pages: 3767 - 3770 Abstract: The development of smart cities has been a significant trend in recent years, aiming to improve the quality of life of citizens by leveraging technology and data. However, the current models of smart cities have limitations in terms of their centralized control and lack of citizen participation. City 5.0 proposes a new paradigm for smart cities that emphasizes the symbiotic relationship between humans and technology. This article presents the concept of Spatial Symbiotic Intelligence, which refers to the ability of a city to dynamically respond to the needs of its citizens and environment through the integration of data from various sources and the use of Decentralized Autonomous Organizations (DAOs) and parallel systems. This letter also discusses the potential benefits and challenges of implementing City 5.0 and highlights some of the ongoing initiatives in this area. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Juanjuan Li;Rui Qin;Cristina Olaverri-Monreal;Radu Prodan;Fei-Yue Wang;
Pages: 3771 - 3774 Abstract: As part of TIV's DHW on Vehicle 5.0, this letter introduces a novel concept, Logistics 5.0, to address high complexities in logistics Cyber-Physical-Social Systems (CPSS). Building upon the theory of parallel intelligence and leveraging advanced technologies and methods such as blockchain, scenarios engineering, Decentralized Autonomous Organizations and Operations (DAOs), Logistics 5.0 promises to accelerate the paradigm shift towards intelligent and sustainable logistics. First, the parallel logistics framework is proposed, and the logistics ecosystem is discussed. Then, the human-oriented operating systems (HOOS) are suggested to providing intelligent Logistics 5.0 solutions. Logistics 5.0 serves as a critical catalyst in realizing the “6S” objectives, i.e. Safety, Security, Sustainability, Sensitivity, Service, and Smartness, within the logistics industry. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhaoliang Zheng;Xu Han;Xin Xia;Letian Gao;Hao Xiang;Jiaqi Ma;
Pages: 3775 - 3780 Abstract: Autonomous driving (AD) and Cooperative Driving Automation (CDA) hold great promise for transforming mobility. However, current off-the-shelf AD or CDA platforms such as Autoware, Apollo, and CARMA, are subject to gaps between simulation and the real world and do not offer integrated pipelines for CDA research, development, and deployment. In this letter, we conceptualize OpenCDA-ROS, building on the strengths of an open-source framework OpenCDA and the Robot Operating System (ROS), to seamlessly synthesize ROS's real-world deployment capabilities with OpenCDA's mature CDA research framework and simulation-based evaluation to fill the gaps aforementioned. OpenCDA-ROS will leverage the advantages of both ROS and OpenCDA to boost the prototyping and deployment of critical CDA features in both simulation and the real world, particularly for cooperative perception, mapping and digital twinning, cooperative decision-making and motion planning, and smart infrastructure services. By offering seamless integration of simulation and real-world CDA, OpenCDA-ROS contributes significantly to advancing fundamental research, development, testing, validation, prototyping, and deployment of autonomous driving and CDA. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Li Wang;Xinyu Zhang;Ziying Song;Jiangfeng Bi;Guoxin Zhang;Haiyue Wei;Liyao Tang;Lei Yang;Jun Li;Caiyan Jia;Lijun Zhao;
Pages: 3781 - 3798 Abstract: Autonomous vehicles require constant environmental perception to obtain the distribution of obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional module as it can simultaneously predict surrounding objects' categories, locations, and sizes. Generally, autonomous vehicles are equipped with multiple sensors, including cameras and LiDARs. The fact that single-modal methods suffer from unsatisfactory detection performance motivates utilizing multiple modalities as inputs to compensate for single sensor faults. Although many multi-modal fusion detection algorithms exist, there is still a lack of comprehensive and in-depth analysis of these methods to clarify how to fuse multi-modal data effectively. Therefore, this paper surveys recent advancements in fusion detection methods. First, we present the broad background of multi-modal 3D object detection and identify the characteristics of widely used datasets along with their evaluation metrics. Second, instead of the traditional classification method of early, middle, and late fusion, we categorize and analyze all fusion methods from three aspects: feature representation, alignment, and fusion, which reveals how these fusion methods are implemented in an essential way. Third, we provide an in-depth comparison of their pros and cons and compare their performance in mainstream datasets. Finally, we further summarize current challenges and research trends for realizing the full potential of multi-modal 3D object detection. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Qingyu Meng;Hongyan Guo;Jialin Li;Qikun Dai;Jun Liu;
Pages: 3799 - 3812 Abstract: Vehicle trajectory prediction plays a vital role in intelligent driving modules and helps intelligent vehicles travel safely and efficiently in complex traffic environments. Several learning-based prediction methods have been developed that accurately identify vehicle behaviour patterns in actual driving data. However, these methods rely on manually curated structured data and are difficult to deploy in intelligent vehicles. In addition, modular information channels that perform vehicle detection, tracking, and prediction tasks encounter error propagation issues and insufficient computing resources. Therefore, this paper proposes a new multitask parallel joint framework in which vehicle detection, state assessment, tracking, and trajectory prediction are performed simultaneously according to raw LIDAR data. Specifically, a multiscale bird's eye view (BEV) backbone feature extraction model is proposed and combined with the designed vehicle state identification branch to distinguish dynamic and static vehicles, which is used as a strong prior for trajectory prediction. In addition, a spatiotemporal pyramid model with convolutions and a backbone residual network is used to generate high definition (HD) maps with strong constraints and guidance capabilities, thereby improving the trajectory prediction accuracy. The experimental results on the real-world dataset nuScenes show that the proposed multitask joint framework outperforms state-of-the-art vehicle detection and prediction schemes, including ES3D and PnPNet. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Jongyong Do;Kyoungseok Han;Seibum B. Choi;
Pages: 3813 - 3825 Abstract: The behavior prediction of the surrounding vehicles is crucial when planning a minimal-risk path when realizing a collision-avoidance system. Herein, we propose a multiple model–based adaptive estimator (MMAE) that infers the lane-change intention of the surrounding vehicles and then predicts their trajectories. Specifically, first, a path is generated in the form of a cubic spline curve using the Frenet coordinate system, which is robust to changes in road curvatures. Linearized recursive least-squares estimation (LRLSE) method is used to adaptively predict a future trajectory based on the past trajectory of the target vehicle. Preview time is defined as a time-varying parameter that determines the final point of the path, and LRLSE updates it in real time. The MMAE applies LRLSEs to multiple paths and obtains the mode probability for each path, then the lane-change intention is inferred using the mode probability and preview time. The predicted future trajectory is the cubic spline curve determined based on the preview time. Further, we verify the performance of our approach using highD, a naturalistic dataset of vehicle trajectories, and compare it with those of existing methods. The proposed method does not require a large amount of data for training and has a low computational burden and high real-time performance. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhenwu Fang;Jinxiang Wang;Zejiang Wang;Jinhao Liang;Yahui Liu;Guodong Yin;
Pages: 3826 - 3838 Abstract: The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. This paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Ping Lang;Daxin Tian;Xuting Duan;Jianshan Zhou;Zhengguo Sheng;Victor C. M. Leung;
Pages: 3839 - 3853 Abstract: Facing the requirements of intelligent vehicles for massive data processing, vehicular edge computing (VEC) utilizes computing resources deployed on the roadside infrastructure to provide proximity computing services for vehicles and forms a novel computing paradigm. Thus, vehicles can reduce the burden of local computing and improve computing efficiency by offloading tasks to roadside computing servers or neighboring resource-idle vehicles for execution via cooperative computation offloading (CO). However, dynamic communication channel states and data handover among multiple VEC servers caused by vehicle mobility pose challenges for CO decision-making and data security. This article applies blockchain to the cooperative CO of VEC and thus proposes a cooperative CO and secure handover framework with a consensus mechanism to guarantee the efficiency of cooperative CO and secure handover. In this framework, models for vehicle mobility and cooperative CO handover are constructed, and a consensus mechanism is proposed. This mechanism ensures the synchronization and immutability of offloaded data in the CO handover. A cooperative CO decision optimization is also formulated considering secure handover with blockchain technology to optimize the latency of vehicular computing tasks. To solve this complex problem, this optimization is transformed into a Markov decision process and a cooperative CO decision algorithm with multiagent deep reinforcement learning is designed, thus achieving the optimal solution. Extensive simulations verified the performance and effectiveness of the proposed method. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Bo Leng;Cheng Tian;Xinchen Hou;Lu Xiong;Wenrui Zhao;Zhuoping Yu;
Pages: 3854 - 3870 Abstract: The tire-road peak adhesion coefficient (TRPAC) is defined as the ratio of the peak adhesion to the vertical load of the tire, which can characterize the ability of a tire to adhere to the road. Reliable TRPAC estimation can not only benefit the vehicle active safety system, but also serve the intelligent transportation system to improve the safety of traffic participants. Considering the problems of low estimation accuracy and poor real-time performance caused by low-quality sensor information in existing TRPAC estimation methods, a TRPAC fusion estimation framework based on the assessment of multisource information quality is proposed in this article. Based on the observability theory of nonlinear systems, a quantitative indicator of dynamic information quality denoted as the excitation level is established. The region of effective excitation is defined as the criterion for starting and stopping the proposed dynamics-based fusion estimator for the longitudinal-lateral coupling condition. Considering occupant comfort and vehicle stability, an active enhancement method based on hierarchical model predictive control is designed to actively improve the excitation level. Based on the receding horizon optimization theory, a dynamics-image-based fusion estimator is proposed to make full use of visual and dynamic information. An adaptive fusion estimation strategy is then proposed to apply the proper estimation working mode according to the multisource information quality. The results of the simulation and vehicle test show that the proposed framework can still perform competitively when the quality of multisource information is poor and achieve reliable TRPAC estimation. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Phillip Karle;Felix Fent;Sebastian Huch;Florian Sauerbeck;Markus Lienkamp;
Pages: 3871 - 3883 Abstract: Reliable detection and tracking of surrounding objects are indispensable for comprehensive motion prediction and planning of autonomous vehicles. Due to the limitations of individual sensors, the fusion of multiple sensor modalities is required to improve the overall detection capabilities. Additionally, robust motion tracking is essential for reducing the effect of sensor noise and improving state estimation accuracy. The reliability of the autonomous vehicle software becomes even more relevant in complex, adversarial high-speed scenarios at the vehicle handling limits in autonomous racing. In this paper, we present a modular multi-modal sensor fusion and tracking method for high-speed applications. The method is based on the Extended Kalman Filter (EKF) and is capable of fusing heterogeneous detection inputs to track surrounding objects consistently. A novel delay compensation approach enables to reduce the influence of the perception software latency and to output an updated object list. It is the first fusion and tracking method validated in high-speed real-world scenarios at the Indy Autonomous Challenge 2021 and the Autonomous Challenge at CES (AC@CES) 2022, proving its robustness and computational efficiency on embedded systems. It does not require any labeled data and achieves position tracking residuals below 0.1 m. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Wei Xue;Zheng Wang;Rencheng Zheng;Xutao Mei;Bo Yang;Kimihiko Nakano;
Pages: 3884 - 3897 Abstract: Automated vehicles require an efficient fail-safe system for unexpected failures in normal operation. Although existing methods aiming at L4 driving automation leverage conservative motion planning to independently guide fail-safe behaviors when a normal planning function fails, they usually ignore human intervention demands. In fact, manual takeover is still a reliable choice of fail-safe maneuver today. Moreover, human drivers tend to handle critical situations, rather than trust the automated system with the vehicle control. Here, we propose a fail-safe architecture for vehicle behavior and motion planning module incorporating shared control in response to the driver intervention. The proposed method not only estimates driver intention to cooperate with the driver on maneuver selection, but also distributes the driving authority in accordance with the types of achievable minimal risk condition. By using the receding horizon planner with composition planning horizons, the driver is able to obtain an appropriate takeover condition on the basis of safety guarantee. Test in the library of critical scenarios shows that the proposed method is effective in terms of safety and assistance to the driver. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Jieyuan Cheng;Yuan Gao;Yongliang Tian;Hu Liu;
Pages: 3898 - 3912 Abstract: Aviation emergency rescue has become one of the most effective means for natural disaster relief due to its flexible and timely characteristics. A reasonable emergency dispatch plan can guarantee the effective implementation of all the rescue measures. Most of previous studies in this area focused on the scheduling and routing but ignored the impact of the specific rescue process, for example the fuel consumption of various helicopters. In this paper, a multi-helicopter-multi-trip Aviation Rescue Routing Problem (ARRP) is analysed which covers the whole rescue process. In addition, a time-domain procedural simulation model is built which can consider different helicopters, refueling or not, various resource locations, multiple disaster sites and other operation factors. Based on that, a Genetic Algorithm (GA) hybridized Large Neighborhood Search (LNS) algorithm (GA-LNS) is proposed for optimization. In ARRP, single search algorithm may lead to the local optimum due to complexity. In contrast, the distance greedy strategy and the load ratio strategy are combined in GA-LNS which can fix the local optimum problem. More specifically, based on the helicopter-tagged-task-sequenced chromosome, the single-point crossover operator is used in GA and then, the worst removal strategy and the first/last insertion strategy are adopted in LNS. Finally, the numerical experiments are exercised to verify the effectiveness of the proposed GA-LNS algorithm which is compared with three traditional basic heuristic algorithms and a stateof-the-art memetic algorithm. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Bo Zhang;Wanzhong Zhao;Chunyan Wang;Yubo Lian;
Pages: 3913 - 3924 Abstract: The development of intelligent driving and X-by-wire chassis technology increases the computational complexity and the number of CAN bus-mounted nodes of steering-by-wire vehicles, which leads to non-negligible large random time delay (TD) of the SbW system's control input. TD has a great impact on SbW system's tracking error, which will inevitably deteriorate the safety and yaw stability of vehicles and even cause sideslip accidents. In order to ensure the safety of SbW vehicles, a layered time-delay robust control strategy (LTDRCS) consisting an upper and a lower controller is proposed. Specifically, a novel Lyapunov-Krasovskii (L-K) TD H∞ controller (H∞C) is designed as the lower controller to reduce tracking error, and the system's stability and convergence are synchronously guaranteed by an L-K function and an H∞ norm constraint. As for the upper controller, a novel terminal sliding mode controller (NTSMC) is established to control the yaw rate index and sideslip angle index. In order to restrain the serious influence of the SbW system's inevitable, unknown, and bounded tracking error caused by TD on vehicle yaw stability and ensure the rapid convergence of the system, a novel integral term is introduced. Simulation and experiments show that the proposed strategy efficiently improves the tracking accuracy of the SbW system and the vehicle yaw stability under random TD condition, which benefits from the synergy of the upper and lower layers. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Tenglong Huang;Jue Wang;Huihui Pan;
Pages: 3925 - 3935 Abstract: Autonomous vehicles equipped with numerous advanced sensors are capable of obtaining road preview information, creating new opportunities for vehicle suspension systems. This article proposes a novel preview suspension control method from adaptive nonlinear control perspectives with less computational burden and is more realistic, unlike optimization-based works or existing linear state-space models-based results that neglected nonlinear terms. The X-shaped bio-inspired dynamics derived from animal or insect skeleton structures are introduced to reduce energy consumption by utilizing beneficial geometrical nonlinearities. Meanwhile, optimal velocity planning approach is investigated to balance vehicle passage time, vibration suppression, and longitudinal comfort by solving a multi-objective optimization problem with the aid of road preview information. Moreover, acceleration constraint reduces the search space and computing requirements, while ensuring planned velocity optimality. Simulation and experiment results are provided to demonstrate the effectiveness and advantages of the constructed energy-saving adaptive preview control framework with constrained velocity planning. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Víctor Jiménez;Jorge Godoy;Antonio Artuñedo;Jorge Villagra;
Pages: 3936 - 3953 Abstract: LiDAR-basedframeworks combining dynamic occupancy grids and object-level tracking are a popular approach for perception of the environment in autonomous driving applications. This paper presents a novel backchannel from the object-level module to the grid-level module that procures the enhancement of overall performance. This feedback leads to an enhanced grid representation by the inclusion of two new steps that allow semantic classification of the occupied space and the improvement of the dynamic estimation. To this end, objects extracted from the grid are analyzed with respect to potential object classes and displacement. Class likelihoods are filtered over time at cell-level using particles and a naive Bayesian classifier. The displacement information is computed taking into account semantic information and comparing objects in consecutive frames. Then, it is used to obtain velocity measurements that are used to enhance grid's dynamic estimation. In contrast to other approaches in the literature seeking similar objectives, this proposal does not rely on additional sensing technologies or neural networks. The evaluation is conducted with real sensor data in challenging urban scenarios. PubDate:
FRI, 18 AUG 2023 14:16:41 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Kathrin Klein;Oliver De Candido;Wolfgang Utschick;
Pages: 3954 - 3961 Abstract: In this article, we address the problem of using non-interpretable Machine Learning (ML) algorithms in safety critical applications, especially automated driving functions. We focus on the lane change prediction of vehicles on a highway. In order to understand wrong decisions, which may lead to accidents, we want to interpret the reasons for a ML algorithm's decision making. To this end, we use motif discovery—a data mining method—to obtain sub-sequences representing typical driving behavior. With the help of these meaningful sub-sequences (motifs), we can study typical driving maneuvers on a highway. On top of this, we propose to replace non-interpretable ML algorithms with an interpretable alternative: a Mixture of Experts (MoE) classifier. We present an MoE classifier consisting of different $k$-Nearest Neighbors ($k$-NN) classifiers trained only on motifs, which represent a few samples from the dataset. These $k$-NN-based experts are fully interpretable, making the lane change prediction fully interpretable, too. Using our proposed MoE classifier, we are able to solve the lane change prediction problem in an interpretable manner. These MoE classifiers show a classification performance comparable to common non-interpretable ML methods from the literature. PubDate:
FRI, 18 AUG 2023 14:16:42 -04 Issue No:Vol. 8, No. 7 (2023)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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