Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle

Wang, Heng; Wang, Bin; Liu, Bingbing; Meng, Xiaohong; Yang, Guang‐Hong

doi:10.1016/j.robot.2016.11.014

Cited by 198 publications

(111 citation statements)

References 20 publications

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“…For instance, autonomous vehicles uses DL models for the detection and localization of obstacles [76], different objects (e.g., vehicles, pedestrians, and bikes, etc.) [77] and their behavior (e.g., tracking pedestrians along the way [78]) and traffic signs [79] and traffic lights recognition [80]. Another prediction tasks in CAVs that involve the application of ML/DL methods are vehicle trajectory and location prediction [81], efficient and intelligent wireless communication [82], and traffic flow prediction and modeling [83].…”

Section: B Applications Of ML For the Prediction Task In Cavsmentioning

confidence: 99%

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

Qayyum

Usama

Qadir

et al. 2020

IEEE Commun. Surv. Tutorials

201

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Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation-which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications-will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings.

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Section: B Applications Of ML For the Prediction Task In Cavsmentioning

confidence: 99%

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

Qayyum

Usama

Qadir

et al. 2020

IEEE Commun. Surv. Tutorials

201

View full text Add to dashboard Cite

show abstract

“…The information about objects around the car can be obtained through various sensors. These include LIDAR and RADAR-based approaches used by [2]. Others include stereo camera-based approaches to make use of the additional depth information available [3].…”

Section: A Related Workmentioning

confidence: 99%

Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images

Baek

Davies

Geng

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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The ability to detect pedestrians and other moving objects is crucial for an autonomous vehicle. This must be done in real-time with minimum system overhead. This paper discusses the implementation of a surround view system to identify moving as well as static objects that are close to the ego vehicle. The algorithm works on 4 views captured by fisheye cameras which are merged into a single frame. The moving object detection and tracking solution uses minimal system overhead to isolate regions of interest (ROIs) containing moving objects. These ROIs are then analyzed using a deep neural network (DNN) to categorize the moving object. With deployment and testing on a real car in urban environments, we have demonstrated the practical feasibility of the solution 1 .

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“…Due to the development of different sensors that provide 3D information, such as 3D LIDAR, stereo vision cameras, and RGB-D cameras, like Kinect, many studies have also looked at human-presence detection using 3D information, such as point-cloud data [15][16][17][18][19][20]. One benefit of using this 3D information is that it is easier to separate the background information from the region of interest as there is a distinct difference in depth information.…”

Section: Related Workmentioning

confidence: 99%

Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots

Saputra¹,

Kormushev²

2018

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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One of the most important features of mobile rescue robots is the ability to autonomously detect casualties, i.e. human bodies, which are usually lying on the ground. This paper proposes a novel method for autonomously detecting casualties lying on the ground using obtained 3D point-cloud data from an on-board sensor, such as an RGB-D camera or a 3D LIDAR, on a mobile rescue robot. In this method, the obtained 3D point-cloud data is projected onto the detected ground plane, i.e. floor, within the point cloud. Then, this projected point cloud is converted into a grid-map that is used afterwards as an input for the algorithm to detect human body shapes. The proposed method is evaluated by performing detections of a human dummy, placed in different random positions and orientations, using an on-board RGB-D camera on a mobile rescue robot called ResQbot. To evaluate the robustness of the casualty detection method to different camera angles, the orientation of the camera is set to different angles. The experimental results show that using the point-cloud data from the on-board RGB-D camera, the proposed method successfully detects the casualty in all tested body positions and orientations relative to the on-board camera, as well as in all tested camera angles.

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Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle

Cited by 198 publications

References 20 publications

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images

Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots

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