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The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes, which can effectively detect the lane markers in various lane road conditions, in driving system for drivers.

Step 1: receiving image: the developed system is able to acquire images from video files. Step 2: splitting image: the system analyzes the splitting process of video file. Step 3: cropping image: specifying the area of interest using crop tool. Step 4: image enhancement: the system conducts the frame to convert RGB color image into grayscale image. Step 5: converting grayscale image to binary image. Step 6: segmenting and removing objects: using the opening morphological operations. Step 7: defining the analyzed area within the image using the Hough transform. Step 8: computing Houghline transform: the system operates the defined segment to analyze the Houghline transform.

This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing. The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi. The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results. The performance of the Hough transform is better than the histogram shapes.

This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm. The concept of this paper is to analyze between algorithms, provide a process of lane detection and search for the algorithm that has the better lane detection results.

The purpose of this paper is to reveal how technology is being applied to augment drivers' skills and improve road safety throughout Europe.

The paper begins with a description of the European Commission's car safety initiatives, and why they are necessary. Then three driver‐assistance systems are examined in detail: adaptive cruise control, lane departure and lane‐changing systems, and driver vigilance monitoring.

Radar, lidar, and imaging sensors are being used and sometimes fused to build highly intelligent driver assistance equipment. The response of the system is crucial to its acceptance and success: false alarms or over‐violent actuation would lead to rejection. Neither must the system encourage over‐confidence. It is estimated that drowsiness detection could prevent 30 per cent of fatal motorway crashes.

The paper alerts engineers and drivers to a long‐term Europe‐wide project to develop and deploy driver assistance technologies.

Most US states exempt police officers from restrictive distracted laws, and most agencies require officers to use mobile data computers while driving. The purpose of this paper is to examine the impact of a text-based distraction task on officer driving performance.

Experienced police patrol officers (n=80) participated in controlled laboratory experiments during which they drove a high-fidelity driving simulator on four separate occasions; twice immediately following five consecutive 10:40 hour patrol shifts (fatigued condition) and again 72 hours after completing the last shift in a cycle (rested condition). In each condition, officers drove identical, counterbalanced 15-minute courses with and without distraction tasks. The research used a within- and between-subjects design.

A generalized linear mixed-model analysis of driving performance showed that officers’ distracted driving performance had significantly greater lane deviation (F=88.58, df=1,308, p < 0.001), instances of unintentionally leaving assigned driving lane (F=64.76, df=1,308, p < 0.001), and braking latency (F=200.82, df=1,308, p < 0.001) than during non-distracted drives. These measures are leading indicators for collision risk.

Simulated driving tasks presented were generally less challenging than patrol driving and likely underestimate the impact of distraction on police driving.

Police officers appear to drive significantly worse while distracted, and their routine experience with using text-based communication devices while driving does not mitigate the risks associated with doing so. Study results suggest that policing organizations should modify policies, practices, training, and technologies to reduce the impact of distraction on officers’ driving. Failing to do so exposes officers and the communities they serve to unnecessary hazards and legal liabilities.

Lane tracking is one of the most important processes for autonomous vehicles because the navigable region usually stands between the lanes, especially in urban environments. A robust lane tracking method is also required for reducing the effect of the noise and the required processing time. The purpose of this paper is to present a new lane tracking method.

A new lane tracking method is presented which uses a partitioning technique for obtaining multiresolution Hough transform of the acquired vision data where Hough transform is one of the most popular algorithms for lane detection. After the detection process, for tracking the detected lanes, a hidden Markov model (HMM) based method is proposed.

The results of the proposed approach show that the partitioned Hough transformation reduces the effect of noise and provides robust lane tracking. In addition, the acquired lanes are successfully tracked by using the designed HMM.

This paper provides a fast lane tracking system which can be integrated with an autonomous vehicle or a driver assistance system.

Reports on a one‐day Institute of Physics seminar on new developments in automotive sensors for hostile environments. Provides details of the nine papers presented.

Safety is one of the most crucial factors in transportation due to so many factors, including human-error, that might result in various small to fatal accidents. This is why lane keeping assist (LKA) is an important system that needs to be developed. LKA will prevent the vehicle from moving to another lane because the driver is negligent to maintain the direction of the vehicle.

In this study, LKA works using camera and ultrasonic sensors. The data coming from these two types of sensors are then fused to determine how should the car reacts to some certain situation so that it stays on the lane. The sensor fusion method was used to further ensure the car's safety.

The results of the hardware simulation show that LKA can successfully avoid the car from getting off the lane with safety index of 95% for high-speed mode and 100% for medium and low-speed mode; meanwhile on the use of ultrasonic sensor, the speed of the car is adjusted to the distance of the car's side disturbance.

This research has succeeded in simulating a sensor fusion–based LKA system.