Robust obstacle detection for advanced driver assistance systems using distortions of inverse perspective mapping of a monocular camera

Prakash, Charan D.; Akhbari, Farshad; Karam, Lina J.

doi:10.1016/j.robot.2018.12.004

Cited by 23 publications

(17 citation statements)

References 24 publications

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“…There are a few approaches to detecting obstacles in a given image. These techniques include convolutional neural networks, inverse perspective mapping, using association and symmetry considerations, or slicing of a disparity matrix (Caltagirone et al 2019;Prakash, Akhbari, and Karam 2019;Wei et al 2019;Zebbara et al 2019;Zhang et al 2019). However, these methods are not suitable for the current research objective.…”

Section: Approachmentioning

confidence: 99%

Obstacle detection and quantification for vehicle mobility in winter conditions

Vecherin¹,

Shaker²,

Parker³

2020

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Recently, the focus for military operations has shifted from the desert to cold climates, causing a corresponding shift in the military's need to better understand the mobility of our current vehicle fleet in these areas. Therefore, this work investigated the effects of winter conditions on military vehicle mobility. The main objective was to detect obstacles on the scene. This study developed and tested a method for automatic obstacle detection in the digital elevation model of a scene. The method detects statistical anomalies relative to an estimated background image that contains no obstacles. The sensitivity of the detection can be adjusted by a specified probability of false alarms, and the obstacle detection confidence is characterized by a probability of detection. The visible height of obstacles above the snow is related to the actual height of the obstacles above the ground. Compared to other detection techniques, the developed method is fast, calibrates itself to the cluttered images, operates with a single given image, and aligns with a detection quantification adopted in the receiver operating characteristic framework. The examples considered in this paper demonstrate high efficiency and applicability of the developed approach to the military vehicle mobility missions.

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Section: Approachmentioning

confidence: 99%

Obstacle detection and quantification for vehicle mobility in winter conditions

Vecherin¹,

Shaker²,

Parker³

2020

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“…R ECENTLY, significant improvements have been reported in the development of vehicular sensors for performing different simple and complex tasks including object detection [1], localization [2], tracking [3], and activity recognition [4] for numerous applications. Such advancements have improved the sensing and computing processes of autonomous driving (AD) [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

Muhammad

Ullah

Lloret

et al. 2021

IEEE Trans. Intell. Transport. Syst.

279

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Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-theart strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

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“…In the past decade, remarkable advances have been realized in various fields such as object detection [1,2], tracking [3,4], control [5], and Vehicle-to-Everything (V2X) communication [6,7] to achieve the goal of autonomous driving. Object detection uses sensors such as cameras, lidar, and radar to detect objects that affect driving.…”

Section: Introductionmentioning

confidence: 99%

Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication

et al. 2021

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Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle.

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Robust obstacle detection for advanced driver assistance systems using distortions of inverse perspective mapping of a monocular camera

Cited by 23 publications

References 24 publications

Obstacle detection and quantification for vehicle mobility in winter conditions

Obstacle detection and quantification for vehicle mobility in winter conditions

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication

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