V2V-Communication, LiDAR System and Positioning Sensors for Future Fusion Algorithms in Connected Vehicles

Eckelmann, Sven; Trautmann, Toralf; Ußler, Hagen; Reichelt, Benjamin; Michler, Oliver

doi:10.1016/j.trpro.2017.12.032

Cited by 31 publications

(21 citation statements)

References 11 publications

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“…To evaluate the performance of the proposed vehicle detection method, we compared the vehicle region selected by the ground-truth method and the vehicle region detected by the proposed method using Equation (3). The performance of the proposed vehicle detector was evaluated using precision and recall.…”

Section: Results Of Vehicle Detectionmentioning

confidence: 99%

“…Automakers are developing self-driving vehicles and are selling vehicles with safe driving devices to support the driver. For safe autonomous driving, a fusion of various technologies is needed along with vehicle IT technology [1][2][3][4]. For vehicle IT technology, the sensors include laser, radar, ultrasonic wave, lidar, charge coupled device (CCD) sensors, and additional devices for communication among vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…An additional problem is that sensors can be easily damaged in slight collisions [5,6]. Moreover, sensors that collect three-dimensional information (such as lidar) are not practical for common use, because the price of a sensor is comparable to the price of a vehicle [3,7]. Therefore, ultrasonic sensors are used to collect two-dimensional information to recognize nearby objects as an auxiliary device for safe driving.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Kim

2019

Symmetry

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In this paper a method for detecting and estimating the distance of a vehicle driving in front using a single black-box camera installed in a vehicle was proposed. In order to apply the proposed method to autonomous vehicles, it was required to reduce the throughput and speed-up the processing. To do this, the proposed method decomposed the input image into multiple-resolution images for real-time processing and then extracted the aggregated channel features (ACFs). The idea was to extract only the most important features from images at different resolutions symmetrically. A method of detecting an object and a method of estimating a vehicle’s distance from a bird’s eye view through inverse perspective mapping (IPM) were applied. In the proposed method, ACFs were used to generate the AdaBoost-based vehicle detector. The ACFs were extracted from the LUV color, edge gradient, and orientation (histograms of oriented gradients) of the input image. Subsequently, by applying IPM and transforming a 2D input image into 3D by generating an image projected in three dimensions, the distance between the detected vehicle and the autonomous vehicle was detected. The proposed method was applied in a real-world road environment and showed accurate results for vehicle detection and distance estimation in real-time processing. Thus, it was showed that our method is applicable to autonomous vehicles.

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Section: Results Of Vehicle Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Kim

2019

Symmetry

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show abstract

“…Thirdly, according to the imaging similarity principle, the coordinates of the feature points in pixel coordinate system can be obtained using the constraint that the collinear relationship and the distance division ratio remain unchanged among three points before and after imaging. Finally, the coordinates of all feature points in LiDAR coordinate system and camera pixel coordinate system are brought into Equation (5) to solve the transformation matrix Q and the intrinsic and external parameters. Through the above three steps, the corresponding coordinates of the selected feature points in LiDAR coordinate system and camera pixel coordinate system can be obtained.…”

Section: Calculation Of the Coordinates Of Feature Points In Camera Pmentioning

confidence: 99%

“…Heterogeneous multi-sensor data fusion has extensive research and applications in mobile robots [1], driverless cars [2], and other fields. Compared with a single-sensor system, multi-sensor fusion systems [3] can provide richer environmental information and complete higher-level tasks such as target detection [4], autonomous location [5], and path planning [6]. The combined application of 3D light detection and ranging (LiDAR) and camera is very common.…”

Section: Introductionmentioning

confidence: 99%

A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

Cai

Weisong

Chen

et al. 2020

Sensors

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Aiming at the problems of feature point calibration method of 3D light detection and ranging (LiDAR) and camera calibration that are calibration boards in various forms, incomplete information extraction methods and large calibration errors, a novel calibration board with local gradient depth information and main plane square corner information (BWDC) was designed. In addition, the "three-step fitting interpolation method" was proposed to select feature points and obtain the corresponding coordinates of feature points in the LiDAR coordinate system and camera pixel coordinate system based on BWDC. Finally, calibration experiments were carried out, and the calibration results were verified by methods such as incremental verification and reprojection error comparison. The calibration results show that using BWDC and the "three-step fitting interpolation method" can solve quite accurate coordinate transformation matrix and intrinsic and external parameters of sensors, which dynamically change within 0.2% in the repeatable experiments. The difference between the experimental value and the actual value in the incremental verification experiment is about 0.5%. The average reprojection error is 1.8312 pixels, and the value changes at different distances do not exceed 0.1 pixels, which also show that the calibration method is accurate and stable.

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A Unified Spatio-Temporal Description Model of Environment for Intelligent Vehicles

Wang

Jiang

Xie

et al. 2020

Lecture Notes in Electrical Engineering

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V2V-Communication, LiDAR System and Positioning Sensors for Future Fusion Algorithms in Connected Vehicles

Cited by 31 publications

References 11 publications

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

A Unified Spatio-Temporal Description Model of Environment for Intelligent Vehicles

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