Fixed Obstacle Detection for Autonomous Vehicle

Fouad, Ayman M.; Sharkawy, R.M.; Onsy, Ahmed

doi:10.1109/cpere45374.2019.8980124

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…However, autonomous vehicles must recognize the obstacles themselves and start moving safely around the detected objects. Several applications and suggestions have emerged that address the use of LiDAR for safe driving [172][173][174][175][176]. Based on the measured data from LiDAR, the algorithms generate an optimized route with adaptation to the current position of the vehicle, the location of obstacles, and the capabilities of the car.…”

Section: Comparisonmentioning

confidence: 99%

Spherical Robots for Special Purposes: A Review on Current Possibilities

Bujňák

Pirník

Rástočný

et al. 2022

Sensors

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The review discusses the possibilities of different driving mechanisms and sensors of spherical robots, and a special kind of mobile robots is introduced and discussed. The sensors discussed can expand robots’ sensing capabilities which are typically very limited. Most spherical robots have holonomic characteristics and protect the inner environment using a shell. Today, there are a diversity of driving mechanisms. Therefore, this article provides a review of all of them and identifies their basic properties. Accordingly, many spherical robots have only inner sensors for moving, balancing, driving, etc. However, a few of them are also equipped with sensors that can measure environmental properties. Therefore, in this paper, we propose the possibility of using such sensors as cameras, LiDARs, thermocouples, and gas sensors, which can be used for special purposes underground, for example, in mines, underground tunnels, or road tunnels. After combining all components are combined, it is possible to design a special type of spherical robot designed for underground exploration, such as accidents in mines or road tunnels.

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Section: Comparisonmentioning

confidence: 99%

Spherical Robots for Special Purposes: A Review on Current Possibilities

Bujňák

Pirník

Rástočný

et al. 2022

Sensors

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“…Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential on the BCV. Obstacle detection techniques can detect obstacles appearing around the vehicle and alert the driver about possible collisions with obstacles [34,35].…”

Section: Laser Ranging Obstacle Detectionmentioning

confidence: 99%

A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

Zhang

Han

et al. 2022

Cogn Comput

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In this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system.

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“…The proposed algorithm is proposed on the four-wheel drive vehicle. 23 The Haar cascade classifier algorithm is preferred for low-cost CPU-based hardware platform whereas, deep learning algorithm is used on GPU-based hardware, such as nano jetson and Intel NUC, in which procurement cost is high. So, in this work, we used Haar cascade classifier algorithm which is easily implemented on the CPU-based low-cost hardware platform (Raspberry Pi).…”

Section: Introductionmentioning

confidence: 99%

A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

Singh

Bera

Chatti³

2022

SIMULATION

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In this paper, an obstacle avoidance and target tracking method for both indoor and outdoor mobile robots in dynamic environment is presented, and it aims to enhance autonomous navigation capability of such robots. In the proposed method, image processing and machine-learning approaches are considered. Since obstacles have differences in color and texture and in order to identify non-navigable areas, a monocular onboard camera is used to capture the road lanes by dealing with an image processing technique. The position of the robot with respect to road lane center during navigation is calculated on the basis of a proposed fuzzy logic rules set. In order to provide fast and robust computation, the Haar cascade classifier-based machine-learning technique has been exploited to detect the different sizes and shapes of the obstacles faced by the robot during its movement from source to destination. The dynamic model of a four-wheel mobile robot is initially developed using bond graph theory and then, the proposed obstacle-avoidance strategy is applied. The effectiveness and performance of the proposed method are tested under various simulation and experimentation scenarios. The behavior of the mobile robot for detecting and avoiding static obstacle for single and double road lane change environment is analyzed and discussed.

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Fixed Obstacle Detection for Autonomous Vehicle

Cited by 4 publications

References 19 publications

Spherical Robots for Special Purposes: A Review on Current Possibilities

Spherical Robots for Special Purposes: A Review on Current Possibilities

A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

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