Vehicle Localization Using Internal Sensors and Low-Cost GPS for Autonomous Driving

The precise measuring of vehicle location has been a critical task in enhancing the autonomous driving in terms of intelligent decision making and safe transportation. Internet of Vehicles ( IoV ) is an important infrastructure in support of autonomous driving, allowing real-time road information exchanging and sharing for localizing vehicles. Global positioning System ( GPS ) is widely used in the traditional IoV system. GPS is unable to meet the key application requirements of autonomous driving due to meter level error and signal deterioration. In this article, we propose a novel solution, named Semi-Direct Monocular Visual-Inertial Odometry using Point and Line Features ( SDMPL-VIO ) for precise vehicle localization. Our SDMPL-VIO model takes advantage of a low-cost Inertial Measurement Unit ( IMU ) and monocular camera, using them as the sensor to acquire the surrounding environmental information. Visual-Inertial Odometry ( VIO ), taking into account both point and line features, is proposed, which is able to deal with both weak texture and dynamic environment. We use a semi-direct method to deal with keyframes and non-keyframes, respectively. Dual sliding window mechanisms can effectively fuse point-line and IMU information. To evaluate our SDMPL-VIO system model, we conduct extensive experiments on both an indoor dataset (i.e., EuRoC) and an outdoor dataset (i.e., KITTI) from the real-world applications, respectively. The experimental results show that the accuracy of SDMPL-VIO proposed by us is better than the mainstream VIO system at present. Especially in the weak texture of the datasets, fast-moving datasets, and other challenging datasets, SDMPL-VIO has a relatively high robustness.

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Fail Safe Process of Vehicle Localization for Reliability Improvement of LV3 Autonomous Driving

International Journal of Automotive Technology ◽

10.1007/s12239-021-0049-8 ◽

2021 ◽

Vol 22 (2) ◽

pp. 529-535

Author(s):

Kyungil Seo ◽

Jaehoon Lee ◽

Je-young Lee ◽

Kyongsu Yi

Keyword(s):

Autonomous Driving ◽

Vehicle Localization ◽

Reliability Improvement ◽

Fail Safe

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Robustness against Chirp Signal Interference of On-Board Vehicle Geodetic and Low-Cost GNSS Receivers

Sensors ◽

10.3390/s21165257 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5257

Author(s):

Franc Dimc ◽

Polona Pavlovčič-Prešeren ◽

Matej Bažec

Keyword(s):

Interference Mitigation ◽

Low Cost ◽

Density Ratio ◽

Autonomous Driving ◽

Signal Interference ◽

Noise Power ◽

Reference Base ◽

Noise Power Spectral Density ◽

Processing Abilities ◽

Gnss Receivers

Robust autonomous driving, as long as it relies on satellite-based positioning, requires carrier-phase-based algorithms, among other types of data sources, to obtain precise and true positions, which is also primarily true for the use of GNSS geodetic receivers, but also increasingly true for mass-market devices. The experiment was conducted under line-of-sight conditions on a straight road during a period of no traffic. The receivers were positioned on the roof of a car travelling at low speed in the presence of a static jammer, while kinematic relative positioning was performed with the static reference base receiver. Interference mitigation techniques in the GNSS receivers used, which were unknown to the authors, were compared using (a) the observed carrier-to-noise power spectral density ratio as an indication of the receivers’ ability to improve signal quality, and (b) the post-processed position solutions based on RINEX-formatted data. The observed carrier-to-noise density generally exerts the expected dependencies and leaves space for comparisons of applied processing abilities in the receivers, while conclusions on the output data results comparison are limited due to the non-synchronized clocks of the receivers. According to our current and previous results, none of the GNSS receivers used in the experiments employs an effective type of complete mitigation technique adapted to the chirp jammer.

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Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

Applied Sciences ◽

10.3390/app11093909 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3909

Author(s):

Changhyeon Park ◽

Seok-Cheol Kee

Keyword(s):

Path Planning ◽

Driving Simulator ◽

Autonomous Driving ◽

Campus Environment ◽

Vehicle Localization ◽

Time Operation ◽

National University ◽

Planning Algorithm ◽

Path Planning Algorithm ◽

Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

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Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽

10.3390/s18103270 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3270 ◽

Cited By ~ 13

Author(s):

Hao Cai ◽

Zhaozheng Hu ◽

Gang Huang ◽

Dunyao Zhu ◽

Xiaocong Su

Keyword(s):

Kalman Filter ◽

Low Cost ◽

Kinematic Model ◽

Intelligent Vehicles ◽

Monocular Vision ◽

Measurement Unit ◽

High Definition ◽

Position Information ◽

Vehicle Localization ◽

Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

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Design of Mechaless LiDAR Optical System With Large FOV Using Liquid Lens and Fisheye Lens

ASME-JSME 2018 Joint International Conference on Information Storage and Processing Systems and Micromechatronics for Information and Precision Equipment ◽

10.1115/isps-mipe2018-8517 ◽

2018 ◽

Author(s):

Hyun Choi ◽

Wan-Chin Kim

Keyword(s):

Low Cost ◽

Autonomous Driving ◽

Voltage Regulation ◽

Field Of View ◽

Fisheye Lens ◽

Liquid Lens ◽

Fine Spatial Resolution ◽

Flash Lidar ◽

Optical Phased Array ◽

Measurable Distance

Mechaless LiDAR technology, which does not have a mechanical drive part, has been actively studied in order to increase the reliability of the LiDAR device at low cost and drive environment in order to more actively apply LiDAR technology to autonomous driving. Mechaless LiDAR technology, which has been mainly studied recently, includes 3D Flash LiDAR technology, MEMS mirror utilization method, and OPA (Optical Phased Array). However, these methods have not been developed rapidly as a key technology for achieving autonomous driving due to low stability of driving environment or remarkably low measurable distance and FOV (field of view) compared with mechanical LiDAR. In this study, we investigated the improvement of FOV by using a flux-deflecting liquid lens and a fisheye lens that can achieve fine spatial resolution through continuous voltage regulation. Based on the initial design results, it was examined that the FOV can be secured to 80 ° or more by utilizing a relatively simple fisheye lens composed of only spherical lenses.

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Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060288 ◽

2019 ◽

Vol 8 (6) ◽

pp. 288 ◽

Cited By ~ 2

Author(s):

Kelvin Wong ◽

Ehsan Javanmardi ◽

Mahdi Javanmardi ◽

Shunsuke Kamijo

Keyword(s):

Point Cloud ◽

Autonomous Vehicle ◽

Autonomous Driving ◽

Geographic Information ◽

Localization Error ◽

Random Forest Regression ◽

Vehicle Localization ◽

Surrounding Environment ◽

Tightly Coupled ◽

Critical Requirement

Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm.

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Performance Evaluation of Autonomous Driving Control Algorithm for a Crawler-Type Agricultural Vehicle Based on Low-Cost Multi-Sensor Fusion Positioning

Applied Sciences ◽

10.3390/app10134667 ◽

2020 ◽

Vol 10 (13) ◽

pp. 4667 ◽

Cited By ~ 1

Author(s):

Joong-hee Han ◽

Chi-ho Park ◽

Jay Hyoun Kwon ◽

Jisun Lee ◽

Tae Soo Kim ◽

...

Keyword(s):

Control Algorithm ◽

Low Cost ◽

Autonomous Driving ◽

Maximum Error ◽

Sensor Technology ◽

Motion Sensor ◽

Integration Algorithm ◽

Sensor Module ◽

Agricultural Vehicle ◽

Mean Square Errors

The agriculture sector is currently facing the problems of aging and decreasing skilled labor, meaning that the future direction of agriculture will be a transition to automation and mechanization that can maximize efficiency and decrease costs. Moreover, interest in the development of autonomous agricultural vehicles is increasing due to advances in sensor technology and information and communication technology (ICT). Therefore, an autonomous driving control algorithm using a low-cost global navigation satellite system (GNSS)-real-time kinematic (RTK) module and a low-cost motion sensor module was developed to commercialize an autonomous driving system for a crawler-type agricultural vehicle. Moreover, an autonomous driving control algorithm, including the GNSS-RTK/motion sensor integration algorithm and the path-tracking control algorithm, was proposed. Then, the performance of the proposed algorithm was evaluated based on three trajectories. The Root Mean Square Errors (RMSEs) of the path-following of each trajectory are calculated to be 9, 7, and 7 cm, respectively, and the maximum error is smaller than 30 cm. Thus, it is expected that the proposed algorithm could be used to conduct autonomous driving with about a 10 cm-level of accuracy.

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Energy Efficiency Due to a Common Global Timebase—Synchronizing FlexRay to 802.1AS Networks as a Foundation

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea8030026 ◽

2018 ◽

Vol 8 (3) ◽

pp. 26

Author(s):

Paul Milbredt ◽

Efim Schick ◽

Michael Hübner

Keyword(s):

Time Synchronization ◽

Fault Tolerant ◽

Low Cost ◽

Autonomous Driving ◽

Consumer Electronics ◽

Area Network ◽

Control Applications ◽

Synchronization Mechanism ◽

Global Clock ◽

Time And Energy

Modern automotive control applications require a holistic time-sensitive development. Nowadays, this is achieved by technologies specifically designed for the automotive domain, like FlexRay, which offer a fault-tolerant time synchronization mechanism built into the protocol. Currently, the automotive industry adopts the Ethernet within the car, not only for embedding consumer electronics, but also as a fast and reliable backbone for control applications. Still, low-cost but highly reliable sensors connected over the traditional Controller Area Network (CAN) deliver data needed for autonomous driving. To fusion the data efficiently among all, a common timebase is required. The alternative would be oversampling, which uses more time and energy, e.g., at least double the perception rates of sensors. Ethernet and CAN do require the latter by default. Hence, a global synchronization mechanism eases tremendously the design of a low power automotive network and is the foundation of a transparent global clock. In this article, we present the first step: Synchronizing legacy FlexRay networks to the upcoming Ethernet backbone, which will contain a precise clock over the generalized Precision Time Protocol (gPTP) defined in IEEE 802.1AS. FlexRay then could still drive its strengths with deterministic transmission behavior and possibly also serve as a redundant technology for fail-operational system design.

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