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Vehicle State Estimation and Localization for Autonomous and Connected Vehicles

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (19 August 2022) | Viewed by 54965

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Special Issue Editors

Prof. Dr. Kichun Jo

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Department of Smart Vehicle Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
Interests: localization; mapping; SLAM; dynamic HD map; sensor fusion; behavior and trajectory planning for autonomous car
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Myoungho Sunwoo

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Guest Editor

Department of Automotive Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: autonomous & connected car; environmental friendly vehicle (ICE, HEV, EV); automotive electronics and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, several companies and research groups have investigated autonomous and connected vehicles to improve the safety, efficiency, and comfort of road users. To operate autonomous and connected vehicles, a vehicle-state estimator is necessary to estimate their own state (e.g., motion, orientation, behavior, and trajectory), as well as the states of other vehicles. Localization can be part of the state estimator which estimates the vehicle pose (position and orientation). The estimator obtains the vehicle state estimates using information from onboard sensors (LiDAR, radar, camera, GPS, IMU, etc.) and communications (in-vehicle networks, wireless networks, etc.) through various theoretical approaches (Bayesian filtering, optimization, machine learning, etc.).
This Special Issue focuses on vehicle-state estimation and localization for connected and autonomous vehicles. We welcome original research contributions and state-of-the-art reviews from academia and industry. The Special Issue topics include but are not limited to:

Vehicle state estimation (e.g., dynamic state, intention, and behavior estimation);
State estimation of other vehicles (e.g., object detection, recognition and tracking, intention and behavior prediction);
Vehicle localization (e.g., odometry, mapping, SLAM, high definition (HD) map);
Sensor-based state estimation and localization (e.g., LiDAR, radar, camera, GPS, IMU);
Communication-based state estimation and localization (e.g., in-vehicle networks, wireless networks);
Theoretical methods for state estimation and localization (e.g., Bayesian filtering, graph-based optimization, machine learning).

Prof. Dr. Kichun Jo
Prof. Dr. Myoungho Sunwoo
Guest Editors

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Keywords

autonomous vehicles
connected vehicles
vehicle dynamic state estimation
vehicle behavior and driver intention estimation
detection and tracking of other vehicles
intention and behavior prediction of surrounding vehicles
localization and mapping
simultaneous localization and mapping (SLAM)
high-definition (HD) maps
HD map management system
state estimation and localization based on sensors (LiDAR, radar, camera, GPS, IMU, etc.)
state estimation and localization based on communication technologies (in-vehicle networks, wireless networks, etc.)
theoretical methods for state estimation and localization (Bayesian filtering, optimization, machine learning, etc.)

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Published Papers (13 papers)

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Research

Jump to: Review

25 pages, 10178 KiB

Open AccessArticle

Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles

by Samir A. Elsagheer Mohamed, Khaled A. Alshalfan, Mohammed A. Al-Hagery and Mohamed Tahar Ben Othman

Sensors 2022, 22(18), 7051; https://doi.org/10.3390/s22187051 - 17 Sep 2022

Cited by 15 | Viewed by 5248

Abstract

Vehicle tailgating or simply tailgating is a hazardous driving habit. Tailgating occurs when a vehicle moves very close behind another one while not leaving adequate separation distance in case the vehicle in front stops unexpectedly; this separation distance is technically called “Assured Clear Distance Ahead” (ACDA) or Safe Driving Distance. Advancements in Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have made it of tremendous significance to have an intelligent approach for connected vehicles to avoid tailgating; this paper proposes a new Internet of Vehicles (IoV) based technique that enables connected vehicles to determine ACDA or Safe Driving Distance and Safe Driving Speed to avoid a forward collision. The technique assumes two cases: In the first case, the vehicle has Autonomous Emergency Braking (AEB) system, while in the second case, the vehicle has no AEB. Safe Driving Distance and Safe Driving Speed are calculated under several variables. Experimental results show that Safe Driving Distance and Safe Driving Speed depend on several parameters such as weight of the vehicle, tires status, length of the vehicle, speed of the vehicle, type of road (snowy asphalt, wet asphalt, or dry asphalt or icy road) and the weather condition (clear or foggy). The study found that the technique is effective in calculating Safe Driving Distance, thereby resulting in forward collision avoidance by connected vehicles and maximizing road utilization by dynamically enforcing the minimum required safe separating gap as a function of the current values of the affecting parameters, including the speed of the surrounding vehicles, the road condition, and the weather condition. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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16 pages, 703 KiB

Open AccessArticle

V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction

by Zhanglong Cao, David Bryant, Timothy C.A. Molteno, Colin Fox and Matthew Parry

Sensors 2021, 21(9), 3215; https://doi.org/10.3390/s21093215 - 6 May 2021

Cited by 6 | Viewed by 2843

Abstract

Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline—which we name the V-spline—that incorporates position and velocity information and a penalty term that controls acceleration. We introduce an adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is proposed, and, in simulation studies, the V-spline shows superior performance to existing methods. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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17 pages, 7502 KiB

Open AccessArticle

Limited Visibility Aware Motion Planning for Autonomous Valet Parking Using Reachable Set Estimation

by Seongjin Lee, Wonteak Lim, Myoungho Sunwoo and Kichun Jo

Sensors 2021, 21(4), 1520; https://doi.org/10.3390/s21041520 - 22 Feb 2021

Cited by 6 | Viewed by 2781

Abstract

Autonomous driving helps drivers avoid paying attention to keeping to a lane or keeping a distance from the vehicle ahead. However, the autonomous driving is limited by the need to park upon the completion of driving. In this sense, automated valet parking (AVP) system is one of the promising technologies for enabling drivers to free themselves from the burden of parking. Nevertheless, the driver must continuously monitor the automated system in the current automation level. The main reason for monitoring the automation system is due to the limited sensor range and occlusions. For safety reasons, the current field of view must be taken into account, as well as to ensure comfort and to avoid unexpected and harsh reactions. Unfortunately, due to parked vehicles and structures, the field of view in a parking lot is not sufficient for considering new obstacles coming out of occluded areas. To solve this problem, we propose a method that estimates the risks for unobservable obstacles by considering worst-case assumptions. With this method, we can ensure to not act overcautiously while moving safe. As a result, the proposed method can be a proactive approach to consider the limited visibility encountered in a parking lot. In the proposed method, occlusion can be efficiently reflected in the planning process. The potential of the proposed method is evaluated in a variety of simulations. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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22 pages, 2298 KiB

Open AccessArticle

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

by Grischa Gottschalg and Stefan Leinen

Sensors 2021, 21(4), 1458; https://doi.org/10.3390/s21041458 - 19 Feb 2021

Cited by 20 | Viewed by 3283

Abstract

High-integrity information about the vehicle’s dynamic state, including position and heading (yaw angle), is required in order to implement automated driving functions. In this work, a comparison of three integrity algorithms for the vehicle dynamic state estimation of a research vehicle for an application in automated driving is presented. Requirements for this application are derived from the literature. All implemented integrity algorithms output a protection level for the position and heading solution. In the comparison, four measurement data sets obtained for the vehicle dynamic state estimation, which is based on a Global Navigation Satellite Signal receiver, inertial measurement units and odometry information (wheel speeds and steering angles), are used. The data sets represent four driving scenarios with different environmental conditions, especially regarding the satellite signal reception. All in all, the Kalman Integrated Protection Level demonstrated the best performance out of the three implemented integrity algorithms. Its protection level bounds the position error within the specified integrity risk in all four chosen scenarios. For the heading error, this also holds true, with a slight exception in the very challenging urban scenario. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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23 pages, 3888 KiB

Open AccessArticle

Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian

by Chenghao Shan, Weidong Zhou, Yefeng Yang and Zihao Jiang

Sensors 2021, 21(1), 198; https://doi.org/10.3390/s21010198 - 30 Dec 2020

Cited by 11 | Viewed by 2435

Abstract

Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of multi-fading factor and an updated monitoring strategy adaptive Kalman filter-based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model and the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the updated monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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26 pages, 11094 KiB

Open AccessArticle

Modular Approach for Odometry Localization Method for Vehicles with Increased Maneuverability

by Chenlei Han, Michael Frey and Frank Gauterin

Sensors 2021, 21(1), 79; https://doi.org/10.3390/s21010079 - 25 Dec 2020

Cited by 1 | Viewed by 3098

Abstract

Localization and navigation not only serve to provide positioning and route guidance information for users, but also are important inputs for vehicle control. This paper investigates the possibility of using odometry to estimate the position and orientation of a vehicle with a wheel individual steering system in omnidirectional parking maneuvers. Vehicle models and sensors have been identified for this application. Several odometry versions are designed using a modular approach, which was developed in this paper to help users to design state estimators. Different odometry versions have been implemented and validated both in the simulation environment and in real driving tests. The evaluated results show that the versions using more models and using state variables in models provide both more accurate and more robust estimation. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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22 pages, 6231 KiB

Open AccessArticle

End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

by Hongyu Hu, Ziyang Lu, Qi Wang and Chengyuan Zheng

Sensors 2020, 20(18), 5443; https://doi.org/10.3390/s20185443 - 22 Sep 2020

Cited by 23 | Viewed by 4031

Abstract

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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20 pages, 5448 KiB

Open AccessArticle

Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis

by Beomjun Kim and Yunju Baek

Sensors 2020, 20(18), 5197; https://doi.org/10.3390/s20185197 - 11 Sep 2020

Cited by 13 | Viewed by 3220

Abstract

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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19 pages, 7434 KiB

Open AccessArticle

Lane Position Detection Based on Long Short-Term Memory (LSTM)

by Wei Yang, Xiang Zhang, Qian Lei, Dengye Shen, Ping Xiao and Yu Huang

Sensors 2020, 20(11), 3115; https://doi.org/10.3390/s20113115 - 31 May 2020

Cited by 13 | Viewed by 3864

Abstract

Accurate detection of lane lines is of great significance for improving vehicle driving safety. In our previous research, by improving the horizontal and vertical density of the detection grid in the YOLO v3 (You Only Look Once, the 3th version) model, the obtained lane line (LL) algorithm, YOLO v3 (S × 2S), has high accuracy. However, like the traditional LL detection algorithms, they do not use spatial information and have low detection accuracy under occlusion, deformation, worn, poor lighting, and other non-ideal environmental conditions. After studying the spatial information between LLs and learning the distribution law of LLs, an LL prediction model based on long short-term memory (LSTM) and recursive neural network (RcNN) was established; the method can predict the future LL position by using historical LL position information. Moreover, by combining the LL information predicted with YOLO v3 (S × 2S) detection results using Dempster Shafer (D-S) evidence theory, the LL detection accuracy can be improved effectively, and the uncertainty of this system be reduced correspondingly. The results show that the accuracy of LL detection can be significantly improved in rainy, snowy weather, and obstacle scenes. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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24 pages, 4599 KiB

Open AccessArticle

Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments

by Dinh Van Nam and Kim Gon-Woo

Sensors 2020, 20(10), 2922; https://doi.org/10.3390/s20102922 - 21 May 2020

Cited by 27 | Viewed by 5104

Abstract

Robotic mapping and odometry are the primary competencies of a navigation system for an autonomous mobile robot. However, the state estimation of the robot typically mixes with a drift over time, and its accuracy is degraded critically when using only proprioceptive sensors in indoor environments. Besides, the accuracy of an ego-motion estimated state is severely diminished in dynamic environments because of the influences of both the dynamic objects and light reflection. To this end, the multi-sensor fusion technique is employed to bound the navigation error by adopting the complementary nature of the Inertial Measurement Unit (IMU) and the bearing information of the camera. In this paper, we propose a robust tightly-coupled Visual-Inertial Navigation System (VINS) based on multi-stage outlier removal using the Multi-State Constraint Kalman Filter (MSCKF) framework. First, an efficient and lightweight VINS algorithm is developed for the robust state estimation of a mobile robot by practicing a stereo camera and an IMU towards dynamic indoor environments. Furthermore, we propose strategies to deal with the impacts of dynamic objects by using multi-stage outlier removal based on the feedback information of estimated states. The proposed VINS is implemented and validated through public datasets. In addition, we develop a sensor system and evaluate the VINS algorithm in the dynamic indoor environment with different scenarios. The experimental results show better performance in terms of robustness and accuracy with low computation complexity as compared to state-of-the-art approaches. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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16 pages, 6326 KiB

Open AccessArticle

Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study

by Rui Fu, Yali Zhang, Chang Wang, Wei Yuan, Yingshi Guo and Yong Ma

Sensors 2020, 20(9), 2683; https://doi.org/10.3390/s20092683 - 8 May 2020

Cited by 4 | Viewed by 3195

Abstract

Speed has an important impact on driving safety, however, this factor is not included in existing safety warning algorithms. This study uses lane change systems to study the influence of vehicle speed on safety warning algorithms, aiming to determine lane change warning rules for different speeds (DS-LCW). Thirty-five drivers are recruited to carry out an extreme trial and naturalistic driving experiment. The vehicle speed, relative speed, relative distance, and minimum safety deceleration (MSD) related to lane change characteristics are then analyzed and calculated as warning rule characterization parameters. Lane change warning rules for a rear vehicle in the target lane under four-speed levels of 60 ≤ v < 70 km/h, 70 ≤ v < 80 km/h, 80 ≤ v < 90 km/h, and v ≥ 90 km/h are established. The accuracy of lane change warning rules not considering speed level (NDS-LCW) and ISO 17387 are found to be 87.5% and 79.8%, respectively. Comparatively, the accuracy rate of DS-LCW under four-speed levels is 94.6%, 93.8%, 90.0%, and 92.6%, respectively, which is significantly superior. The algorithm proposed in this paper provides warning in the lane change process with a smaller relative distance, and the accuracy rate of DS-LCW is significantly superior to NDS-LCW and ISO 17387. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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30 pages, 16199 KiB

Open AccessArticle

Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas

by Kyu-Won Kim and Gyu-In Jee

Sensors 2020, 20(4), 1220; https://doi.org/10.3390/s20041220 - 23 Feb 2020

Cited by 9 | Viewed by 3832

Abstract

We propose a free-resolution probability distributions map (FRPDM) and an FRPDM-based precise vehicle localization method using 3D light detection and ranging (LIDAR). An FRPDM is generated by Gaussian mixture modeling, based on road markings and vertical structure point cloud. Unlike single resolution or multi-resolution probability distribution maps, in the case of the FRPDM, the resolution is not fixed and the object can be represented by various sizes of probability distributions. Thus, the shape of the object can be represented efficiently. Therefore, the map size is very small (61 KB/km) because the object is effectively represented by a small number of probability distributions. Based on the generated FRPDM, point-to-probability distribution scan matching and feature-point matching were performed to obtain the measurements, and the position and heading of the vehicle were derived using an extended Kalman filter-based navigation filter. The experimental area is the Gangnam area of Seoul, South Korea, which has many buildings around the road. The root mean square (RMS) position errors for the lateral and longitudinal directions were 0.057 m and 0.178 m, respectively, and the RMS heading error was 0.281°. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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Review

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30 pages, 552 KiB

Open AccessReview

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

by Hoofar Shokravi, Hooman Shokravi, Norhisham Bakhary, Mahshid Heidarrezaei, Seyed Saeid Rahimian Koloor and Michal Petrů

Sensors 2020, 20(11), 3274; https://doi.org/10.3390/s20113274 - 8 Jun 2020

Cited by 53 | Viewed by 8114

Abstract

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques. Full article

(This article belongs to the Special Issue Vehicle State Estimation and Localization for Autonomous and Connected Vehicles)

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