Drivable Region Estimation for Self-Driving Vehicles Using Radar

Hussain, Muhammad Ishfaq; Azam, Shoaib; Rafique, Muhammad Aasim; Sheri, Ahmad Muqeem; Jeon, Moongu

doi:10.1109/tvt.2022.3161378

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…Consumer vehicles are equipped with basic safety features: lane departure warning, forward collision warning, and blindspot detection using ADAS [7]. New automobiles are stepping up a level and providing lane-centering assistants, rear-cross alert, and experimental self-park systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System

Hussain

Rafique

Kim

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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A frequent cause of auto accidents is disregarding the proximal traffic of an ego-vehicle during lane changing. Presumably, in a split-second-decision situation we may prevent an accident by predicting the intention of a driver before her action onset using the neural signals data, meanwhile building the perception of surroundings of a vehicle using optical sensors. The prediction of an intended action fused with the perception can generate an instantaneous signal that may replenish the driver's ignorance about the surroundings. This study examines electromyography (EMG) signals to predict intention of a driver along perception building stack of an autonomous driving system (ADS) in building an advanced driving assistant system (ADAS). EMG are classified into left-turn and right-turn intended actions and lanes and object detection with camera and Lidar are used to detect vehicles approaching from behind. A warning issued before the action onset, can alert a driver and may save her from a fatal accident. The use of neural signals for intended action prediction is a novel addition to camera, radar and Lidar based ADAS systems. Furthermore, the study demonstrates efficacy of the proposed idea with experiments designed to classify online and offline EMG data in real-world settings with computation time and the latency of communicated warnings.

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System

Hussain

Rafique

Kim

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Especially in the field of autonomous driving, the fusion of radar and camera is of great importance. For example, Hussain et al [21] designed a low-cost method for detecting drivable areas in long-distance areas of self-driving cars by fusing radar and camera. Similarly, Wu et al [22] solved the challenge of missing parking boundaries on maps or difficult parking spot detection by jointly using radar and cameras.…”

Section: Fusion Of Radar and Camera Applicationsmentioning

confidence: 99%

Radar-Camera Fusion Network for Depth Estimation in Structured Driving Scenes

Li,

Yan,

Chen

et al. 2023

Sensors

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Depth estimation is an important part of the perception system in autonomous driving. Current studies often reconstruct dense depth maps from RGB images and sparse depth maps obtained from other sensors. However, existing methods often pay insufficient attention to latent semantic information. Considering the highly structured characteristics of driving scenes, we propose a dual-branch network to predict dense depth maps by fusing radar and RGB images. The driving scene is divided into three parts in the proposed architecture, each predicting a depth map, which is finally merged into one by implementing the fusion strategy in order to make full use of the potential semantic information in the driving scene. In addition, a variant L1 loss function is applied in the training phase, directing the network to focus more on those areas of interest when driving. Our proposed method is evaluated on the nuScenes dataset. Experiments demonstrate its effectiveness in comparison with previous state of the art methods.

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“…The approaches either use features of the point clouds [53, 54] or the spatial distribution of the point clouds [55] to identify noisy data from false and interference objects. Commonly used algorithms include intra‐frame clustering [56] and DBSCAN [57]. Fusing the data of MMW radars and other sensors (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

Lei

Zhao

et al. 2022

IET Intelligent Trans Sys

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Millimetre‐wave (MMW) radars have been increasingly deployed along the roadside highways to collect vehicle trajectory data, which are valuable for traffic safety analyses and traffic control decisions. Nevertheless, possible errors of the trajectories resulting from roadside MMW radars have not been well documented. This study scrutinizes roadside MMW radar data and the concurrent video ground truth to identify five typical errors of vehicle trajectories, including different vehicles tagged with the same ID, different trajectories produced by the same vehicle, ghost vehicles, erroneous vehicle classification, and vehicle location drift. Data cleaning methods were developed to identify and correct these errors. The performance of the developed methods was evaluated empirically in a case study based on the data of five radars, which were installed at different locations along the highway and had different headings. The thresholds of the developed method were calibrated using one radar data and then applied to the data of the five radars. The results showed that the data qualities of all radars are greatly improved by the developed methods, demonstrating that the developed methods are generally applicable for cleaning vehicle trajectory data from roadside MMW radars.

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Drivable Region Estimation for Self-Driving Vehicles Using Radar

Cited by 10 publications

References 31 publications

Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System

Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System

Radar-Camera Fusion Network for Depth Estimation in Structured Driving Scenes

Identifying and correcting the errors of vehicle trajectories from roadside millimetre‐wave radars

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