Object Modeling from 3D Point Cloud Data for Self-Driving Vehicles

Azam, Shoaib; Munir, Farzeen; Rafique, Aasim; Ko, Yeongmin; Sheri, Ahmad Muqeem; Jeon, Moongu

doi:10.1109/ivs.2018.8500500

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…To introduce the concept of road event, our new approach to situation awareness and the ROAD dataset to the computer vision and AV communities, some of us have organised in October 2021 the workshop "The ROAD challenge: Event Detection for Situation Awareness in Autonomous Driving" 6 . For the challenge, we selected (among the tasks described in Sec.…”

Section: The Road Challengementioning

confidence: 99%

“…The latest generation of robot-cars is equipped with a range of different sensors (i.e., laser rangefinders, radar, cameras, GPS) to provide data on what is happening on the road [5]. The information so extracted is then fused to suggest how the vehicle should move [6], [7], [8], [9]. Some authors, however, maintain that vision is a sufficient sense for AVs to navigate their environment, supported by humans' ability to do just so.…”

Section: Introductionmentioning

confidence: 99%

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ROAD: The Road Event Awareness Dataset for Autonomous Driving

Singh

Akrigg

Maio

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.

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Section: The Road Challengementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ROAD: The Road Event Awareness Dataset for Autonomous Driving

Singh

Akrigg

Maio

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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“…In literature, much work has been carried out to use Lidar data for the detection and classification of the object. Azam et al have used multiple frames of Lidar to generate the CAD models, and then employs the convolution neural network architecture for the detection and classification of point cloud data [ 40 , 41 , 42 , 43 ]. Bo Li et al have used a convolution neural network for the object detection and classification in point cloud data [ 44 ].…”

Section: Architecturementioning

confidence: 99%

System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

Azam

Munir

Sheri

et al. 2020

Sensors

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In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.

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“…Traffic scene recognition from input images is one of the fundamental technologies for Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS) applications, including object modeling [1], semantic segmentation [2], object recognition [3]- [5], localization and mapping [6]- [8], etc. It is perceived as an essential key-step towards understanding traffic scenes and serves to eliminate the gap between resulting performance and visual reasoning capability of human beings.…”

Section: Introductionmentioning

confidence: 99%

“…A naïve algorithm capable of performing multiple tasks simultaneously is to employ a suite of independent networks, one for each task. However, simply training a single network for a particular task, no matter how adequately optimized on each task, is far from satisfactory in terms of performance on 1 Machine Learning & Vision Laboratory, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, South Korea {brightyoun, jihyo, mgjeon}@gist.ac.kr 2 Curtin University, Kent St, Bently WA 6102, Australia jm.andrew. yu@gmail.com Fig.…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles

Lee

Jeon

et al. 2020

Preprint

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Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevant task independently from one another, never considering the entire system as a whole. Because of this, they are limited to utilizing a task-specific set of features for all possible tasks of inference-time, which ignores the capability to leverage common task-invariant contextual knowledge for the task at hand. To address this problem, we propose an algorithm to jointly learn the task-specific and shared representations by adopting a multi-task learning network. Specifically, we present a lower bound for the mutual information constraint between shared feature embedding and input that is considered to be able to extract common contextual information across tasks while preserving essential information of each task jointly. The learned representations capture richer contextual information without additional task-specific network. Extensive experiments on the large-scale dataset HSD demonstrate the effectiveness and superiority of our network over state-of-the-art methods.

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Object Modeling from 3D Point Cloud Data for Self-Driving Vehicles

Cited by 12 publications

References 12 publications

ROAD: The Road Event Awareness Dataset for Autonomous Driving

ROAD: The Road Event Awareness Dataset for Autonomous Driving

System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles

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