Vehicle Localization using 76GHz Omnidirectional Millimeter-Wave Radar for Winter Automated Driving

Yoneda, Keisuke; Hashimoto, Naoya; Yanase, Ryo; Aldibaja, Mohammad; Suganuma, Naoki

doi:10.1109/ivs.2018.8500378

Cited by 41 publications

(36 citation statements)

References 9 publications

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“…At present, the sensing sensors used in autonomous vehicles mainly include Lidar, millimeter-wave radar, ultrasonic radar, and camera [2][3][4][5]. Lidar can scan and measure by transmitting laser pulses to generate a precise map of road scene topography, which can be used for short-distance and long-distance obstacle detection.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception.

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Section: Introductionmentioning

confidence: 99%

“…Two overlapping cases between two rectangles with the same IoU values. IoU calculation principle 4. Journal of Sensors of China is 440 mm.…”

mentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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“…This vehicle had various functions necessary to enable automated driving in an urban area, and has actually been running in Japan for several years. In previous works, real-time localization algorithms have been developed using different types of sensors such as 3-D LiDARs, cameras or MWRs [7,29,30]. Therefore, it is assumed that the precise vehicle pose has been estimated using such localization algorithms in this evaluation.…”

Section: Conditionmentioning

confidence: 99%

“…On the other hand, in automated driving using HD maps, the self-localization module precisely estimates the vehicle pose by map-matching using a range sensor or image sensor [6][7][8]. Generally, position accuracy of approximately 0.1 to 0.2 m is considered to be necessary for decision-making and path planning in automated driving.…”

Section: Introductionmentioning

confidence: 99%

Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving

Yoneda

Kuramoto

Suganuma

et al. 2020

Sensors

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Traffic light recognition is an indispensable elemental technology for automated driving in urban areas. In this study, we propose an algorithm that recognizes traffic lights and arrow lights by image processing using the digital map and precise vehicle pose which is estimated by a localization module. The use of a digital map allows the determination of a region-of-interest in an image to reduce the computational cost and false detection. In addition, this study develops an algorithm to recognize arrow lights using relative positions of traffic lights, and the arrow light is used as prior spatial information. This allows for the recognition of distant arrow lights that are difficult for humans to see clearly. Experiments were conducted to evaluate the recognition performance of the proposed method and to verify if it matches the performance required for automated driving. Quantitative evaluations indicate that the proposed method achieved 91.8% and 56.7% of the average f-value for traffic lights and arrow lights, respectively. It was confirmed that the arrow-light detection could recognize small arrow objects even if their size was smaller than 10 pixels. The verification experiments indicate that the performance of the proposed method meets the necessary requirements for smooth acceleration or deceleration at intersections in automated driving.

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“…In automatic driving scene, vehicles need to obtain not only their own accurate position information but also position information of other surrounding vehicles to furtherly assure safety [9]. In response to this, autonomous vehicles should be equipped with laser radar, millimeter-wave radar, binocular camera, and other sensing devices to detect surroundings, yet such devices are vulnerable to environmental interference, which could result in enormous error [10], [11]. The key to solving this problem is to connect autonomous vehicles to the network and introduce Road Side Unit (RSU) to automatic driving roads [12], [13].…”

Section: Introductionmentioning

confidence: 99%

PCM: A Positioning Calibration Mechanism for Connected Autonomous Vehicles

Ling¹,

Chu²,

Lu³

et al. 2020

IEEE Access

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Connected autonomous driving is considered as a potential way to improve traffic safety. However, when positioning information is delivered through network, the network time delay could result in additional positioning errors, which has been proved by the experiment done in this paper in XuChang automatic driving center that the influence of one-way time delay will lead to meter-scale positioning error. In this paper, we propose a positioning calibration mechanism for connected autonomous vehicles called PCM. PCM firstly estimates the network time delay and then saves the calibrated location information in the MEC server to reduce the positioning error caused by network time delay. Experiments in FangShan and XuChang automatic driving centers verify the performance of PCM, and the result shows that PCM can reduce the positioning error to centimeter-level. For example, under 100ms time delay, the average positioning error without PCM is 119.67cm, while the average positioning error with PCM is 3.03cm. Experimental results show that PCM can effectively reduce the positioning error caused by a one-way time delay to centimeter-level, which is strictly required for positioning autonomous vehicles in automatic driving. INDEX TERMS Automatic driving, high precision positioning, one-way time delay, the transformation of a coordinate system, mobile edge computing.

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Vehicle Localization using 76GHz Omnidirectional Millimeter-Wave Radar for Winter Automated Driving

Cited by 41 publications

References 9 publications

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving

PCM: A Positioning Calibration Mechanism for Connected Autonomous Vehicles

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