The focus of this paper is inter-vehicles distance measurement which is a very important and challenging task in image processing domain. Where it is used in several systems such as Driving Safety Support Systems (DSSS), autonomous driving and traffic mobility. In the current paper, we propose an inter-vehicle distance measurement system for self-driving based on image processing. The proposed system uses two cameras mounted as one stereo camera, in the hosting vehicle behind the rear-view mirror. The detection of vehicles is performed first in a single camera using a recent powerful work from the literature. Then, the same vehicle is detected in the image captured by the second camera using template matching technique. Thus, the inter-vehicle distance is calculated using a simple method based on the position of the vehicle in both cameras, geometric derivations and additional technical data such as distance between the cameras and some other specific angles (e.g. the cameras view field angle). The results of the extensive experiments showed the high accuracy of the proposed method compared to the previous works from literature and it allows to measure efficiently the distances between the vehicles and the hosting vehicle. In addition, this method could be used in several systems of various domains in real time regardless of the object types. The experiments results were done on a Hardware Processor System (HPS) located in a VEEK-MT2S provided by TERASIC.

Distance measurement system for autonomous vehicles using stereo camera

Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we proposed a fast and robust lane detection method by combining a semantic segmentation network and an optical flow estimation network. Specifically, the whole research was divided into three parts: lane segmentation, lane discrimination, and mapping. In terms of lane segmentation, a robust semantic segmentation network was proposed to segment key frames and a fast and slim optical flow estimation network was used to track non-key frames. In the second part, density-based spatial clustering of applications with noise (DBSCAN) was adopted to discriminate lanes. Ultimately, we proposed a mapping method to map lane pixels from pixel coordinate system to camera coordinate system and fit lane curves in the camera coordinate system that are able to provide feedback for autonomous driving. Experimental results verified that the proposed method can speed up robust semantic segmentation network by three times at most and the accuracy fell 2% at most. In the best of circumstances, the result of the lane curve verified that the feedback error was 3%.

A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation

Autonomous Surface Vehicle (ASV) is a vehicle that is operated in the water surface without any person in the vehicle. Since there is no person in the ASV, a motion controller is essentially needed. The control system is used to make sure that the water vehicle is moving at the desired speed. In this paper, we use a Touristant ASV with the following specifications: the length is 4 meters, the diameter is 1.625 meters, and the height is 1.027 meters. The main contribution of this paper is applying the Sliding Mode Control system to the Touristant ASV model under the influence of environmental factors. The environmental factors considered in this work are wind speed and wave height. The Touristant ASV model is nonlinear and uses three degree of freedom (DOF), namely surge, sway and yaw. The simulation results show that the performance of the closed-loop system by using the SMC method depends on the environmental factors. If environmental factors are higher, then the resulting error is also higher. The average error difference between those resulted from the simulation without environmental factors and those with the influence of environmental factors is 0.05% for surge, sway and yaw motions.

/pdf/sliding-mode-control-design-for-autonomous-surface-vehicle-4fsqwhft6s.pdf

Sliding mode control design for autonomous surface vehicle motion under the influence of environmental factor

The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.

http://ijai.iaescore.com/index.php/IJAI/article/download/20658/pdf

Vehicle detection and tracking for traffic management

In this paper, the performance of Linear Antenna Array Element (LAAE) has been evaluated at the Base Station (BS) with a different number of elements for Unmanned Air Vehicle UAV application. The Switched Beam (SB) and Phase Array (PA) have been used as a steering beam mechanism. The beam steering tracker is based on the GPS points of the UAV and the BS. In addition, the Misalignment angle has been analyzed for SB and PA corresponding to the maximum speed of the UAV. The compression between SB and PA in term of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) and BER vs. Misalignment angle have been examined by using Matlab. The results show that the PA has better performance than SB in both terms under Additive White Gaussian Noise (AWGN) channel with an interference signal. When the number of the elements is eight provides longer distance than four by the factor (1.5 in SB case and 2 in PA case) and wider Misalignment angle range than twelve by factor (2 in SW case and 3 in PA case). Therefore, it is becoming a useful option for many applications.

/pdf/performance-assessment-of-antenna-array-for-an-unmanned-air-4qlfefiw9v.pdf

Performance assessment of antenna array for an unmanned air vehicle

Unmanned Aerial Vehicles or commonly known as drones are better suited for "dull, dirty, or dangerous" missions than manned aircraft. The drone can be either remotely controlled or it can travel as per predefined path using complex automation algorithm built during its development. In general, Unmanned Aerial Vehicle (UAV) is the combination of Drone in the air and control system on the ground. Design of an UAV means integrating hardware, software, sensors, actuators, communication systems and payloads into a single unit for the application involved. To make it completely autonomous, the most challenging problem faced by UAVs is obstacle avoidance. In this paper, a novel method to detect frontal obstacles using monocular camera is proposed. Computer Vision algorithms like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are used to detect frontal obstacles and then distance of the obstacle from camera is calculated. To meet the defined objectives, designed system is tested with self-developed videos which are captured by DJI Phantom 4 pro.

/pdf/obstacle-avoidance-and-distance-measurement-for-unmanned-595v6adqoq.pdf

Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision

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Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision