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IEEE Transactions on Intelligent Transportation Systems
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Year: 2023 | Volume: 24 | Issue: 11
IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY
Year: 2023 | Volume: 24 | Issue: 11
Scanning the Issue
Year: 2023 | Volume: 24 | Issue: 11
Resilient Countermeasures Against Cyber-Attacks on Self-Driving Car Architecture
Junaid M. Qurashi;Kamal Jambi;Fawaz Alsolami;Fathy E. Eassa;Maher Khemakhem;Abdullah Basuhail
Publication Year: 2023,Page(s):11514 - 11543
Cited by: Papers (2)
Self-driving cars, believed to be on track to become a seven trillion-dollar industry by 2050, are likely to be a popular mode of transportation in the near future, as the technology promises safety and efficiency. Significant advances have been made to achieve these objectives. However, the idea of a self-driving car completing its goals without human intervention in the presence of uncertainties...Show More
Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review
Mahsa Golchoubian;Moojan Ghafurian;Kerstin Dautenhahn;Nasser Lashgarian Azad
Publication Year: 2023,Page(s):11544 - 11567
Cited by: Papers (28)
Planning an autonomous vehicle’s (AV) path in a space shared with pedestrians requires reasoning about pedestrians’ future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle’s interactions with the pedestrians on pedestrians’ future motion behaviours. In this regard, this paper systematically reviews different methods...Show More
Vehicle Detection for Autonomous Driving: A Review of Algorithms and Datasets
Publication Year: 2023,Page(s):11568 - 11594
Cited by: Papers (14)
Nowadays, vehicles with a high level of automation are being driven everywhere. With the apparent success of autonomous driving technology, we keep working to achieve fully autonomous vehicles on roads. Efficient and accurate vehicle detection is one of the essential tasks in the environment perception of an autonomous vehicle. Therefore, numerous algorithms for vehicle detection have been develop...Show More
Adaptive Sliding Mode Fault Tolerant Control for Autonomous Vehicle With Unknown Actuator Parameters and Saturated Tire Force Based on the Center of Percussion
Zhongchao Liang;Mingyu Shen;Jing Zhao;Zhongguo Li;Yongfu Wang;Zhengtao Ding
Publication Year: 2023,Page(s):11595 - 11606
Cited by: Papers (6)
With consideration of tire force saturation in vehicle motions, a novel path-following controller is developed for autonomous vehicles with unknown-bound disturbances and unknown actuator parameters. An adaptive sliding-mode fault-tolerant control (ASM-FTC) strategy is designed to stabilize the path-following errors without any information of disturbance boundaries, actuator fault boundaries and s...Show More
Computing Arterial Travel Time Distributions From Loop Detector and Probe Datasets
Rahul Sengupta;Rohith R. K. Reddy;Parth Shah;James Dika;Xiaohui Huang;Anand Rangarajan;Sanjay Ranka
Publication Year: 2023,Page(s):11607 - 11622
Estimating Arterial Travel Time distributions from high-resolution loop detector data (signal events and vehicle detection events) is a challenging task. Even though high-resolution loop detector data for several years may be available, the lack of other data modes and ground-truth labels, hinders approaches that rely on additional information. Among the approaches relying exclusively on loop dete...Show More
Deep Multi-Agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
Dong Chen;Mohammad R. Hajidavalloo;Zhaojian Li;Kaian Chen;Yongqiang Wang;Longsheng Jiang;Yue Wang
Publication Year: 2023,Page(s):11623 - 11638
Cited by: Papers (53)
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the mixed-traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) problem, where the AVs (on both merge lane and through lane) collaboratively learn a policy to adapt to HDVs to maximize the ...Show More
A Centrality Approach to Select Offloading Data Aggregation Points in Vehicular Sensor Networks
Douglas L. L. Moura;Geymerson S. Ramos;Andre L. L. Aquino;Antonio A. F. Loureiro
Publication Year: 2023,Page(s):11639 - 11653
This work proposes a centrality-based approach to identify data offloading points in a VSN. The solution presents a scheme to select vehicles used as aggregation points to collect and aggregate other vehicles’ data before uploading it to processing stations. We evaluate the proposed solution in a realistic simulation scenario derived from data traffic containing more than 700,000 individual car tr...Show More
Stochastic Velocity Prediction for Connected Vehicles Considering V2V Communication Interruption
Lihua Wang;Fengqi Zhang;Yahui Cui;Serdar Coskun;Xiaolin Tang;Yalian Yang;Xiaosong Hu
Publication Year: 2023,Page(s):11654 - 11667
Cited by: Papers (1)
Reliable and accurate velocity prediction can significantly contribute to the quality of connected vehicle control applications. Existing efforts focus on the velocity prediction without considering vehicle-to-vehicle (V2V) communication interruption. Hence, a stochastic velocity prediction method for connected vehicles considering V2V communication interruption is put forward for the first time. ...Show More
Interaction-Aware Labeled Multi-Bernoulli Filter
Nida Ishtiaq;Amirali Khodadadian Gostar;Alireza Bab-Hadiashar;Reza Hoseinnezhad
Publication Year: 2023,Page(s):11668 - 11681
Cited by: Papers (6)
Tracking multiple objects through time is an important part of an intelligent transportation system. Random finite set (RFS)-based filters are one of the emerging techniques for tracking multiple objects. In multi-object tracking (MOT), a common assumption is that each object is moving independent of its surroundings. However, in many real-world applications, objects interact with one another and ...Show More
A Human-Like Trajectory Planning Method on a Curve Based on the Driver Preview Mechanism
Jian Zhao;Dongjian Song;Bing Zhu;Zhuo Sun;Jiayi Han;Yuhang Sun
Publication Year: 2023,Page(s):11682 - 11698
Cited by: Papers (35)
With the development of intelligent vehicle technology, many studies have been focused on developing human-like trajectory planning methods for automated driving systems. Although data-driven methods are widely used for human driver behavior learning, there have been fewer studies on realizing human-like trajectory planning by using the generation mechanism of driving behavior, especially under cu...Show More
Evaluation of the Connected Vehicle Environment Effectiveness at Tunnel Entrance
Jianhua Zhang;Xiaohua Zhao;Haijian Li;Jianyu Qi;Guanyang Xing
Publication Year: 2023,Page(s):11699 - 11709
Cited by: Papers (5)
The change of space environment at the tunnel entrance which the light changes from bright to dark will lead to frequent traffic accidents. To explore whether the connected vehicle environment will improve the driving performance and traffic safety at the tunnel entrance, this paper first of all designed a driving simulation experiment and provided driving aids through the Human-Machine Interface ...Show More
A Dynamic Programming Model for Joint Optimization of Electric Drayage Truck Operations and Charging Stations Planning at Ports
Publication Year: 2023,Page(s):11710 - 11719
Cited by: Papers (5)
The adoption of electric vehicles at ports is a promising approach to achieve sustainability goals. However, realizing the full potential of this strategy depends on effective coordination between infrastructure planning and operational scheduling. In this paper, we propose a joint optimization framework that can co-optimize these two components to minimize the overall system cost. To capture the ...Show More
Using Tabu Search to Avoid Concave Obstacles for Source Location
Junqi Zhang;Huan Liu;Peng Zu;Mengshi Zhao;Cheng Wang;Aiiad Albeshri;Abdullah Abusorrah;MengChu Zhou
Publication Year: 2023,Page(s):11720 - 11732
Cited by: Papers (6)
Recently, using a particle swarm optimizer (PSO) to guide robots in a source location problem has attracted widespread interest. While being navigated by PSO, robots are easily trapped into U-shape-like concave obstacles such that they move back and forth cyclically and fail to locate a correct source. Existing obstacle avoidance strategies perform well when robots have information about all obsta...Show More
Experimental Study and Modeling of the Lower-Level Controller of Automated Vehicle
Hua-Qing Liu;Shi-Teng Zheng;Rui Jiang;Junfang Tian;Ruidong Yan;Fang Zhang;Dezhao Zhang
Publication Year: 2023,Page(s):11733 - 11742
Cited by: Papers (2)
Accurate modeling of lower-level controller plays an important role in the traffic flow of automated vehicles (AVs). However, there lacks enough attention with this respect. To address this issue, we conduct a field experiment with two vehicles that are equipped with developable autonomous driving system, where one can customize the upper-level control algorithm. Based on the field experimental da...Show More
Ship Collision Avoidance Navigation Signal Recognition via Vision Sensing and Machine Forecasting
Qilin Bi;Miaohui Wang;Yijing Huang;Minling Lai;Zhijun Liu;Xiuying Bi
Publication Year: 2023,Page(s):11743 - 11755
Cited by: Papers (4)
Ship collision avoidance (SCA) is an important technique in the field of decision-making in marine navigation. Although some promising solutions have been developed recently, there is still the lack of low-cost and reliable sensing equipment. Inspired by the low-cost of camera sensors and the success of machine learning, this paper designs a vision-based method to recognize ships and their micro-f...Show More
DeepCAN: Hybrid Method for Road Type Classification Using Vehicle Sensor Data for Smart Autonomous Mobility
Publication Year: 2023,Page(s):11756 - 11772
Cited by: Papers (1)
Usually, image- and radar-based data are used to perform environmental characteristics related tasks in autonomous cars, while the use of valuable sensor data from the Controller Area Network (CAN) bus has been limited. The vehicle’s CAN bus data consist of multivariate time series data, such as velocity, RPM, and acceleration, which contain meaningful information about the vehicle dynamics and en...Show More
A Real-World Reinforcement Learning Framework for Safe and Human-Like Tactical Decision-Making
Publication Year: 2023,Page(s):11773 - 11784
Cited by: Papers (3)
Lane-change decision-making for vehicles is a challenging task for many reasons, including traffic rules, safety, and the stochastic nature of driving. Because of its success in solving complex problems, deep reinforcement learning (DRL) has been suggested for addressing these issues. However, the studies on DRL to date have gone no further than validation in simulation and failed to address what ...Show More
Interactive Decision-Making With Switchable Game Modes for Automated Vehicles at Intersections
Shizheng Jia;Yuxiang Zhang;Xin Li;Xiaoxiang Na;Yuhai Wang;Bingzhao Gao;Bing Zhu;Rongjie Yu
Publication Year: 2023,Page(s):11785 - 11799
Cited by: Papers (3)
Interactive decision-making between multiple automated vehicles under unsigned intersections is a high-level dynamic decision-making scenario, greatly increasing the complexity of decision-making. In this situation, making the decision-making manner in accordance with the logic of human and guaranteeing driving safety is technically challenging. A multi-factor-enabled interactive decision-making m...Show More
Neighborhood Rough Residual Network–Based Outlier Detection Method in IoT-Enabled Maritime Transportation Systems
Qiong Chen;Liangru Xie;Lirong Zeng;Sining Jiang;Weiping Ding;Xiaomeng Huang;Hao Wang
Publication Year: 2023,Page(s):11800 - 11811
Cited by: Papers (6)
Outlier detection can identify anomalies in large-scale data. To provide reliability and security for Internet of Things (IoT)-enabled maritime transportation systems (MTSs), in this paper we propose an outlier detection method based on the neighborhood rough residual network (NRRN). We calculate the neighborhood approximation accuracy and neighborhood conditional entropy to obtain the neighborhoo...Show More
Characterizing the Uncertainty of Link Progression Speed Using Low-Frequency Probe Vehicle Data
Publication Year: 2023,Page(s):11812 - 11822
Link progression speed is a key characteristic of urban traffic flow and is essential to developing effective signal coordination schemes. Typically, it is empirically determined as a fixed value regardless of its stochastic nature. Previous studies have proposed both analytical and simulation models to investigate the vehicle progression delay caused by different sources (i.e., midblock traffic, ...Show More
Acoustic SLAM With Moving Sound Event Based on Auxiliary Microphone Arrays
Publication Year: 2023,Page(s):11823 - 11834
Cited by: Papers (2)
Acoustic simultaneous localization and mapping (ASLAM) aim to map the positions of sound sources while passively localizing the microphone array embedded in the robot platform. In this paper, an ASLAM method with auxiliary microphone arrays based on dual interacting multiple models and unscented Kalman filter (D-IMM-UKF) is proposed for the single moving source scenario. Firstly, a dual-unscented ...Show More
A Virtual Method for Optimizing Deployment of Roadside Monitoring Lidars at As-Built Intersections
Publication Year: 2023,Page(s):11835 - 11849
Cited by: Papers (2)
Roadside monitoring Lidars (RMLs) will be a crucial part of the future intelligent transportation system. Current approaches for optimizing RMLs’ placement at intersections work in hypothetical environments which do not well reflect real-world situations. This article proposes a new virtual method (VM) for optimizing the deployment of RMLs at as-built intersections. The proposed VM operates in a v...Show More
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