Experimental studies of autonomous driving of a vehicle on the road using LiDAR and DGPS

Kim, Jeong Ku; Kim, Jin Wook; Jung, Tae Hyung; Park, Young Jun; Ko, Yun-Ho; Jung, Seul

doi:10.1109/iccas.2015.7364852

Cited by 23 publications

(8 citation statements)

References 2 publications

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“…A The moving object model The motion state of the peripheral target of the driverless vehicle is uncertain, so we establish a consistent model for the peripheral target. [11][12][13][14]We actually simplify the actual moving form of the peripheral target. It is assumed that the model object moves along a straight line and can also move at a fixed turning rate and a constant speed, as shown in Figure 5.…”

Section: Methodsmentioning

confidence: 99%

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CAEV-Surrounding Objects Detection and Tracking for Autonomous Driving Using Lidar and Radar Fusion

Liu

Cai

2020

Preprint

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Radar and Lidar are two environmental sensors commonly used in autonomous vehicles，Lidars are accurate in determining objects’ positions but significantly less accurate on measuring their velocities. However, Radars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. In order to compensate for the low detection accuracy, incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LIDAR, we proposed an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles. By employing the Unscented Kalman Filter, radar and LIDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle. Finally, we do a variety of driving environment under the real car algorithm verification test. The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy. Compared with a single sensor, it has obvious advantages and can improve the intelligence level of driverless cars.

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Section: Methodsmentioning

confidence: 99%

“…Then according to each predicted value and weight, the Predicted measurement mean and measurement mean Covariance can be obtained. As shown in equations (11) and (12).…”

mentioning

confidence: 99%

CAEV-Surrounding Objects Detection and Tracking for Autonomous Driving Using Lidar and Radar Fusion

Liu

Cai

2020

Preprint

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“…Since its emergence, LiDAR has been rapidly applied to the acquisition of threedimensional space information, providing a new technical solution for three-dimensional modeling of cities [1], exploration and detection of geology and roads [2,3], autonomous driving and unmanned driving of vehicles [4,5], etc. The 3D point cloud data acquired by LiDAR can represent the contour information of terrain and buildings.…”

Section: Introductionmentioning

confidence: 99%

Distance–Intensity Image Strategy for Pulsed LiDAR Based on the Double-Scale Intensity-Weighted Centroid Algorithm

Yan

Yang

et al. 2021

Remote Sensing

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We report on a self-adaptive waveform centroid algorithm that combines the selection of double-scale data and the intensity-weighted (DSIW) method for accurate LiDAR distance–intensity imaging. A time window is set to adaptively select the effective data. At the same time, the intensity-weighted method can reduce the influence of sharp noise on the calculation. The horizontal and vertical coordinates of the centroid point obtained by the proposed algorithm are utilized to record the distance and echo intensity information, respectively. The proposed algorithm was experimentally tested, achieving an average ranging error of less than 0.3 ns under the various noise conditions in the listed tests, thus exerting better precision compared to the digital constant fraction discriminator (DCFD) algorithm, peak (PK) algorithm, Gauss fitting (GF) algorithm, and traditional waveform centroid (TC) algorithm. Furthermore, the proposed algorithm is fairly robust, with remarkably successful ranging rates of above 97% in all tests in this paper. Furthermore, the laser echo intensity measured by the proposed algorithm was proved to be robust to noise and to work in accordance with the transmission characteristics of LiDAR. Finally, we provide a distance–intensity point cloud image calibrated by our algorithm. The empirical findings in this study provide a new understanding of using LiDAR to draw multi-dimensional point cloud images.

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“…Among many techniques, the Differential GNSS (DGNSS) technique using pseudo-range correction (PRC) has been widely used in a number of fields because it can improve positioning accuracy in real time using a low-cost receiver. Recently, DGNSS positioning algorithms are considered as a critical component of sensor fusion for autonomous vehicle navigation [ 1 , 2 , 3 ]. To improve positioning accuracies, quite many studies of coupled GNSS and multi-sensors integration [ 3 , 4 ] and reduction of the multipath error [ 1 , 5 ] have been published.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, DGNSS positioning algorithms are considered as a critical component of sensor fusion for autonomous vehicle navigation [ 1 , 2 , 3 ]. To improve positioning accuracies, quite many studies of coupled GNSS and multi-sensors integration [ 3 , 4 ] and reduction of the multipath error [ 1 , 5 ] have been published. GLONASS, BeiDou, and Galileo signals in addition to GPS are used in multi-GNSS positioning to improve accuracy, reliability, and availability of DGNSS positioning [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time

Sohn

Park

Tae

2017

Sensors

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We developed and validated a pseudo-range correction (PRC) modeling system that can prevent degradation of positioning accuracy even in situations where one cannot obtain PRC messages for Differential Global Navigation Satellite System (DGNSS). A PRC modeling scheme was devised based on the repeat time of GNSS satellites and previously-collected PRC data. The difference between the modeled and real PRC values observed at the reference station showed a bias error of about ±1.0 m and a root mean square error (RMSE) less than 1.5 m. When we applied the predicted PRC to Differential Global Positioning System (DGPS) and Differential BeiDou (DBDS) positioning, horizontal RMSE values were at a level of 1.0 m, while vertical RMSE was in the range of 1.8–3.0 m. We found that modelled PRCs can provide positioning results similar to those based on real PRCs and can provide significant improvement over standalone positioning without PRCs.

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Experimental studies of autonomous driving of a vehicle on the road using LiDAR and DGPS

Cited by 23 publications

References 2 publications

CAEV-Surrounding Objects Detection and Tracking for Autonomous Driving Using Lidar and Radar Fusion

CAEV-Surrounding Objects Detection and Tracking for Autonomous Driving Using Lidar and Radar Fusion

Distance–Intensity Image Strategy for Pulsed LiDAR Based on the Double-Scale Intensity-Weighted Centroid Algorithm

Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time

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Experimental studies of autonomous driving of a vehicle on the road using LiDAR and DGPS

Cited by 23 publications

References 2 publications

CAEV-Surrounding Objects Detection and&nbsp;Tracking for Autonomous Driving Using Lidar and Radar Fusion

CAEV-Surrounding Objects Detection and&nbsp;Tracking for Autonomous Driving Using Lidar and Radar Fusion

Distance–Intensity Image Strategy for Pulsed LiDAR Based on the Double-Scale Intensity-Weighted Centroid Algorithm

Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time

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CAEV-Surrounding Objects Detection and Tracking for Autonomous Driving Using Lidar and Radar Fusion

CAEV-Surrounding Objects Detection and Tracking for Autonomous Driving Using Lidar and Radar Fusion