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IEEE Transactions on Intelligent Transportation Systems
Popular Documents - February 2025
Includes the 50 most frequently accessed documents for this publication.
Date
February 2025
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Khan Muhammad;Amin Ullah;Jaime Lloret;Javier Del Ser;Victor Hugo C. de Albuquerque
Publication Year: 2021,Page(s):4316 - 4336
Cited by: Papers (280)
Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control proce...Show More
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Ling Zhao;Yujiao Song;Chao Zhang;Yu Liu;Pu Wang;Tao Lin;Min Deng;Haifeng Li
Publication Year: 2020,Page(s):3848 - 3858
Cited by: Papers (1606)
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capt...Show More
Deep Reinforcement Learning for Autonomous Driving: A Survey
B Ravi Kiran;Ibrahim Sobh;Victor Talpaert;Patrick Mannion;Ahmad A. Al Sallab;Senthil Yogamani;Patrick Pérez
Publication Year: 2022,Page(s):4909 - 4926
Cited by: Papers (943)
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computat...Show More
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
Publication Year: 2022,Page(s):740 - 759
Cited by: Papers (314)
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...Show More
Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues
Mohammad Reza Rezaee;Nor Asilah Wati Abdul Hamid;Masnida Hussin;Zuriati Ahmad Zukarnain
Publication Year: 2024,Page(s):6397 - 6426
Cited by: Papers (5)
In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on e...Show More
Graph Neural Networks for Intelligent Transportation Systems: A Survey
Saeed Rahmani;Asiye Baghbani;Nizar Bouguila;Zachary Patterson
Publication Year: 2023,Page(s):8846 - 8885
Cited by: Papers (46)
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future ...Show More
The State of Urban Air Mobility Research: An Assessment of Challenges and Opportunities
Sheikh Shahriar Ahmed;Grigorios Fountas;Virginie Lurkin;Panagiotis Ch. Anastasopoulos;Yu Zhang;Michel Bierlaire;Fred Mannering
Publication Year: 2025,Page(s):1375 - 1394
Electric vertical take-off and landing aircraft-based urban air mobility (UAM) service in conjunction with personal flying cars are anticipated to offer mobility benefits in terms of reduced travel time, alleviate demand from overburdened ground transportation systems; and bring forth a paradigm shift in travel patterns. Furthermore, uncrewed aerial vehicles or drones have significant potential in...Show More
A Novel Framework Combining MPC and Deep Reinforcement Learning With Application to Freeway Traffic Control
Publication Year: 2024,Page(s):6756 - 6769
Cited by: Papers (4)
Model predictive control (MPC) and deep reinforcement learning (DRL) have been developed extensively as two independent techniques for traffic management. Although the features of MPC and DRL complement each other very well, few of the current studies consider combining these two methods for application in the field of freeway traffic control. This paper proposes a novel framework for integrating ...Show More
Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
Khan Muhammad;Tanveer Hussain;Hayat Ullah;Javier Del Ser;Mahdi Rezaei;Neeraj Kumar;Mohammad Hijji;Paolo Bellavista;Victor Hugo C. de Albuquerque
Publication Year: 2022,Page(s):22694 - 22715
Cited by: Papers (55)
Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research ...Show More
Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
Khan Muhammad;Tanveer Hussain;Hayat Ullah;Javier Del Ser;Mahdi Rezaei;Neeraj Kumar;Mohammad Hijji;Paolo Bellavista;Victor Hugo C. de Albuquerque
Year: 2022 | Volume: 23 | Issue: 12
Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook
Ziying Song;Lin Liu;Feiyang Jia;Yadan Luo;Caiyan Jia;Guoxin Zhang;Lei Yang;Li Wang
Publication Year: 2024,Page(s):15407 - 15436
Cited by: Papers (13)
In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning. The key step to this system is related to 3D object detection that utilizes vehicle-mounted sensors such as LiDAR and cameras to identify the size, the category, and the location of nearby objects. De...Show More
Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review
Sarang Thombre;Zheng Zhao;Henrik Ramm-Schmidt;José M. Vallet García;Tuomo Malkamäki;Sergey Nikolskiy;Toni Hammarberg;Hiski Nuortie;M. Zahidul H. Bhuiyan;Simo Särkkä;Ville V. Lehtola
Publication Year: 2022,Page(s):64 - 83
Cited by: Papers (124)
Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (...Show More
Intelligent Transportation Systems Using Roadside Infrastructure: A Literature Survey
Publication Year: 2024,Page(s):6309 - 6327
Cited by: Papers (17)
The main problems in transportation are accidents, increasingly slow traffic flow, and pollution. An intelligent transportation system (ITS) using roadside infrastructure can overcome these problems. For this reason, the number of such systems is increasing dramatically, and therefore requires an adequate overview. To the best of our knowledge, no current systematic review of existing ITS solution...Show More
A Review of Motion Planning Techniques for Automated Vehicles
David González;Joshué Pérez;Vicente Milanés;Fawzi Nashashibi
Publication Year: 2016,Page(s):1135 - 1145
Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on com...Show More
Automotive LiDAR Technology: A Survey
Publication Year: 2022,Page(s):6282 - 6297
Cited by: Papers (206)
Nowadays, and more than a decade after the first steps towards autonomous driving, we keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being a key instrument for the success of this technology. Such advances trigger the emergence of new players in the automotive industry, and along with car manufacturers, this sector represents a multibillion-dollar market where e...Show More
A Survey of Battery Swapping Stations for Electric Vehicles: Operation Modes and Decision Scenarios
Publication Year: 2022,Page(s):10163 - 10185
Cited by: Papers (79)
The population of electric vehicles (EVs) has grown rapidly over the past decade due to the development of EV technologies, battery materials, charger facilities, and public charging services. Many governments have implemented plans to ban fossil fuel vehicles considering the significance of EVs in reducing greenhouse gas emissions. However, due to the battery material characteristics and charger ...Show More
Multi-Sensor Fusion Technology for 3D Object Detection in Autonomous Driving: A Review
Publication Year: 2024,Page(s):1148 - 1165
Cited by: Papers (21)
With the development of society, technological progress, and new needs, autonomous driving has become a trendy topic in smart cities. Due to technological limitations, autonomous driving is used mainly in limited and low-speed scenarios such as logistics and distribution, shared transport, unmanned retail, and other systems. On the other hand, the natural driving environment is complicated and unp...Show More
The Crossroads of LLM and Traffic Control: A Study on Large Language Models in Adaptive Traffic Signal Control
Publication Year: 2025,Page(s):1701 - 1716
Recent advancements in Large Language Models (LLMs) have ushered in opportunities to craft agents that exhibit human-like cognitive abilities, notably reasoning and planning. Leveraging the vast knowledge bases and sophisticated understanding of interrelations inherent in LLMs, this study explores their application in the dynamic and complex realm of traffic management. Specifically, we focus on A...Show More
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Publication Year: 2015,Page(s):865 - 873
Cited by: Papers (1296)
Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. T...Show More
A Comprehensive Survey on Conflict Detection and Resolution in Unmanned Aircraft System Traffic Management
Publication Year: 2025,Page(s):1395 - 1418
The anticipated proliferation of Unmanned Aerial Vehicles (UAVs) in the airspace in the coming years has raised concerns about how to manage their flights to avoid collisions and crashes at various stages of flight. To this end, many Unmanned Aircraft Traffic Management systems (UTM) have been designed. These systems use various methods for managing UAV conflicts. Several surveys have reviewed con...Show More
Safety-Aware Human-in-the-Loop Reinforcement Learning With Shared Control for Autonomous Driving
Publication Year: 2024,Page(s):16181 - 16192
Cited by: Papers (1)
The learning from intervention (LfI) approach has been proven effective in improving the performance of RL algorithms; nevertheless, existing methodologies in this domain tend to operate under the assumption that human guidance is invariably devoid of risk, thereby possibly leading to oscillations or even divergence in RL training as a result of improper demonstrations. In this paper, we propose a...Show More
Vision-Based Anti-UAV Detection and Tracking
Publication Year: 2022,Page(s):25323 - 25334
Cited by: Papers (77)
Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. Several detection and tracking systems for UAVs have been introduced in recent years, but most of them are based on radio frequency, radar, and other media. We assume that the field of computer vision is mature enough to detect and track invading UAVs. Thus...Show More
A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective
Wenhao Ding;Chejian Xu;Mansur Arief;Haohong Lin;Bo Li;Ding Zhao
Publication Year: 2023,Page(s):6971 - 6988
Cited by: Papers (69)
Autonomous driving systems have witnessed significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms. One critical challenge for their massive deployment in the real world is their safety evaluation. Most existing driving systems are still trained and evaluated on naturalistic scenarios collected from daily life or heurist...Show More
A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions
Publication Year: 2024,Page(s):19365 - 19398
Cited by: Papers (1)
Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely without human intervention. AD agents generate driving policies based on online perception results, which are crucial to the realization of safe, efficient, and comfortable driving behaviors, particularly in high-dimensional and stochastic traffic scenarios. Currently, deep reinforcement learning (DRL) techniqu...Show More
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice
Publication Year: 2020,Page(s):900 - 918
Cited by: Papers (492)
One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between vehicles and pedestrians, the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depen...Show More
The Road to Safe Automated Driving Systems: A Review of Methods Providing Safety Evidence
Magnus Gyllenhammar;Gabriel Rodrigues de Campos;Martin Törngren
Publication Year: 2025,Page(s):1 - 31
In recent years, enormous investments in Automated Driving Systems (ADSs) have distinctly advanced ADS technologies. Despite promises made by several high profile auto-makers, it has however become clear that the challenges involved for deploying ADS have been drastically underestimated. Contrary to previous generations of automotive systems, common design, development, verification and validation...Show More
Vehicle-Road-Cloud Collaborative Perception Framework and Key Technologies: A Review
Bolin Gao;Jiaxi Liu;Hengduo Zou;Jiaxing Chen;Lei He;Keqiang Li
Publication Year: 2024,Page(s):19295 - 19318
Cited by: Papers (1)
Over recent years, the Vehicle-Road-Cloud Integration System (VRCIS) and Intelligent and Connected Vehicles (ICVs) have gained significant attention in the realm of autonomous driving. By sharing data across diverse traffic participants and coordinating with VRCIS, ICVs can achieve enhanced perception accuracy and superior driving decisions, surpassing autonomous vehicles that rely solely on onboa...Show More
Deep Learning for Inertial Positioning: A Survey
Publication Year: 2024,Page(s):10506 - 10523
Cited by: Papers (10)
Inertial sensors are widely utilized in smartphones, drones, vehicles, and wearable devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors’ measurements are inevitably corrupt...Show More
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Di Feng;Christian Haase-Schütz;Lars Rosenbaum;Heinz Hertlein;Claudius Gläser;Fabian Timm;Werner Wiesbeck;Klaus Dietmayer
Publication Year: 2021,Page(s):1341 - 1360
Cited by: Papers (725)
Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal...Show More
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method
Publication Year: 2022,Page(s):15729 - 15741
Cited by: Papers (83)
Curvy roads are a particular type of urban road scenario, wherein the curvature of the road centerline changes drastically. This paper is focused on the trajectory planning task for autonomous driving on a curvy road. The prevalent on-road trajectory planners in the Frenet frame cannot impose accurate restrictions on the trajectory curvature, thus easily making the resultant trajectories beyond th...Show More
Machine Learning to Solve Vehicle Routing Problems: A Survey
Aigerim Bogyrbayeva;Meraryslan Meraliyev;Taukekhan Mustakhov;Bissenbay Dauletbayev
Publication Year: 2024,Page(s):4754 - 4772
Cited by: Papers (7)
This paper provides a systematic overview of machine learning methods applied to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both the machine learning and operations research communities in solving VRPs either through pure learning methods or by combining them with traditional handcrafted heuristics. We present a taxonomy of studies on learning parad...Show More
Urban Air Mobility: History, Ecosystem, Market Potential, and Challenges
Publication Year: 2021,Page(s):6074 - 6087
Cited by: Papers (194)
Since the early 20th century, inventors have conceptualized “plane cars” and other urban aerial transportation. Emerging innovations in electrification, automation, and other technologies are enabling new opportunities for on-demand air mobility, business models, and aircraft design. Urban air mobility (UAM) envisions a safe, sustainable, affordable, and accessible air transportation system for pa...Show More
Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control
Publication Year: 2020,Page(s):1086 - 1095
Cited by: Papers (561)
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, the centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. The multi-agent RL (MARL) overcomes the scalability issue by distributi...Show More
Embedded Real-Time Vehicle and Pedestrian Detection Using a Compressed Tiny YOLO v3 Architecture
Laura Falaschetti;Lorenzo Manoni;Lorenzo Palma;Paola Pierleoni;Claudio Turchetti
Publication Year: 2024,Page(s):19399 - 19414
Vehicle and pedestrian detection (VaPD) is one of the most critical tasks in an advanced driver assistance system which help the driver to drive safely and save the pedestrian life. VaPD is a typical object detection problem that requires a trade-off among accuracy, speed, and memory consumption. Most existing methods focus on improving detection accuracy, while ignoring VaPD requires real-time de...Show More
Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes
Wenhao Fan;Yi Su;Jie Liu;Shenmeng Li;Wei Huang;Fan Wu;Yuan’an Liu
Publication Year: 2023,Page(s):4277 - 4292
Cited by: Papers (85)
In an internet of vehicle (IoV) scenario, vehicular edge computing (VEC) exploits the computing capabilities of the vehicles and roadside unit (RSU) to enhance the task processing capabilities of the vehicles. Resource management is essential to the performance improvement of the VEC system. In this paper, we propose a joint task offloading and resource allocation scheme to minimize the total task...Show More
LiDAR-BEVMTN: Real-Time LiDAR Bird’s-Eye View Multi-Task Perception Network for Autonomous Driving
Sambit Mohapatra;Senthil Yogamani;Varun Ravi Kumar;Stefan Milz;Heinrich Gotzig;Patrick Mäder
Publication Year: 2025,Page(s):1547 - 1561
LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception has the largest body of literature after camera perception. However, multi-task learning across tasks like detection, segmentation, and motion estimation using LiDAR remains relatively unexplored, especially on automotive-grade embedded platforms. We present a real-time multi-task convolutional neural network f...Show More
Learning-Based Stochastic Model Predictive Control for Autonomous Driving at Uncontrolled Intersections
Publication Year: 2025,Page(s):1538 - 1546
Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving obstacles, such as the position other vehicles will take while crossing an uncontrolled intersection. We address this problem by proposing a stochastic model predictive control (MPC) approach with robust collision avoidance constraints to guarantee safety. By adopting a stochastic...Show More
A Survey on Cyber-Security of Connected and Autonomous Vehicles (CAVs)
Publication Year: 2022,Page(s):6240 - 6259
Cited by: Papers (171)
As the general development trend of the automotive industry, connected and autonomous vehicles (CAVs) can be used to increase transportation safety, promote mobility choices, reduce user costs, and create new job opportunities. However, with the increasing level of connectivity and automation, malicious users are able to easily implement different kinds of attacks, which threaten the security of C...Show More
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
Zhichao Han;Yuwei Wu;Tong Li;Lu Zhang;Liuao Pei;Long Xu;Chengyang Li;Changjia Ma;Chao Xu;Shaojie Shen;Fei Gao
Publication Year: 2024,Page(s):1797 - 1814
Cited by: Papers (18)
As a fundamental component of autonomous driving systems, motion planning has garnered significant attention from both academia and industry. This paper focuses on efficient and spatial-temporal optimal trajectory optimization in unstructured environments using compact convex approximations of vehicle shapes. Conventional approaches typically model the task as an optimal control problem by discret...Show More
Autonomous Vehicles Security: Challenges and Solutions Using Blockchain and Artificial Intelligence
Gueltoum Bendiab;Amina Hameurlaine;Georgios Germanos;Nicholas Kolokotronis;Stavros Shiaeles
Publication Year: 2023,Page(s):3614 - 3637
Cited by: Papers (55)
The arrival of autonomous vehicles (AVs) promises many great benefits, including increased safety and reduced energy consumption, pollution, and congestion. However, these engines have many security and privacy issues that could undermine the expected benefits if not addressed. AVs will provide new opportunities for hackers to carry out malicious attacks, posing a great threat to the future of mob...Show More
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
Anton Kuznietsov;Balint Gyevnar;Cheng Wang;Steven Peters;Stefano V. Albrecht
Publication Year: 2024,Page(s):19342 - 19364
Cited by: Papers (6)
Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, highly complex AI systems exacerbate the existing challenge of safety assurance of AD. One way to mitigate this challenge is to utilize explainable AI (XAI) techniques. To this end, we present the first...Show More
Transformer-Based Reinforcement Learning for Scalable Multi-UAV Area Coverage
Dezhi Chen;Qi Qi;Qianlong Fu;Jingyu Wang;Jianxin Liao;Zhu Han
Publication Year: 2024,Page(s):10062 - 10077
Cited by: Papers (4)
Compared with terrestrial networks, unmanned aerial vehicles (UAVs) have the characteristics of flexible deployment and strong adaptability, which are an important supplement to intelligent transportation systems (ITS). In this paper, we focus on the multi-UAV network area coverage problem (ACP) which require intelligent UAVs long-term trajectory decisions in the complex and scalable network envir...Show More
Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges
Varun Ravi Kumar;Ciarán Eising;Christian Witt;Senthil Kumar Yogamani
Publication Year: 2023,Page(s):3638 - 3659
Cited by: Papers (46)
Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as th...Show More
MetroLoc: Metro Vehicle Mapping and Localization With LiDAR-Camera-Inertial Integration
Publication Year: 2025,Page(s):1441 - 1453
In this paper, we propose an accurate and robust multi-modal sensor fusion framework, MetroLoc, towards one of the most extreme scenarios, the large-scale metro environments. MetroLoc is built atop an IMU-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, and inertial information with the convenience of loosely coupled methods. The proposed framework is compo...Show More
GNSS Position Integrity in Urban Environments: A Review of Literature
Publication Year: 2018,Page(s):2762 - 2778
Cited by: Papers (295)
Integrity is one criteria to evaluate GNSS performance, which was first introduced in the aviation field. It is a measure of trust which can be placed in the correctness of the information supplied by the total system. In recent years, many GNSS-based applications emerge in the urban environment including liability critical ones, so the concept of integrity attracts more and more attention from ur...Show More
Optimization-Based Trajectory Planning for Autonomous Parking With Irregularly Placed Obstacles: A Lightweight Iterative Framework
Bai Li;Tankut Acarman;Youmin Zhang;Yakun Ouyang;Cagdas Yaman;Qi Kong;Xiang Zhong;Xiaoyan Peng
Publication Year: 2022,Page(s):11970 - 11981
Cited by: Papers (80)
This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with...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 (49)
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
Detecting stress during real-world driving tasks using physiological sensors
Publication Year: 2005,Page(s):156 - 166
This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for ...Show More
A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System
Publication Year: 2024,Page(s):5554 - 5566
Cited by: Papers (19)
With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using onl...Show More
Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review
Publication Year: 2023,Page(s):10279 - 10301
Cited by: Papers (18)
The prediction of pedestrian behavior is essential for automated driving in urban traffic and has attracted increasing attention in the vehicle industry. This task is challenging because pedestrian behavior is driven by various factors, including their individual properties, the interactions with other road users, and the interactions with the environment. Deep learning approaches have become incr...Show More
A Comprehensive Survey on Traffic Missing Data Imputation
Yimei Zhang;Xiangjie Kong;Wenfeng Zhou;Jin Liu;Yanjie Fu;Guojiang Shen
Publication Year: 2024,Page(s):19252 - 19275
Intelligent Transportation Systems (ITS) are essential and play a key role in improving road safety, reducing congestion, optimizing traffic flow and facilitating the development of smart cities. The collection of data from ITS and its transformation into information is challenged by the presence of missing data in datasets. Timely and effective handling of missing data is crucial to facilitate in...Show More
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