Impact Assessment of Driver Distraction by Cellphone on Start-up Lost-time and Average Saturation Headway at Signalized Intersections Based on Vehicle Position in the Queue

Cellphone usage has a significant impact on signalized intersections' capacity and level of service. This study investigated the impact of cellphone usage on signalized intersection capacity and level of service in Dammam Metropolitan Area, Saudi Arabia. The data included 183 useful cycles and 2407 start-up lost time and average saturation headway values at cycles with cellphone usage and cycles without cellphone usage at 24 signalized intersections. The main hypothesis of the study is that cellphone usage increases the start-up lost time at signalized intersection capacity. The secondary hypothesis is that cellphone usage increases the average saturation headway at signalized intersections. Normal distribution and z-test were conducted to assess whether there is a significant increase in average saturation headway and start-up lost time. The study found a significant increase in start-up lost time of about 0.7 seconds but found no significant increase in average saturation headway due to cellphone usage. Also, start-up lost time increases as vehicles of cellphone users get closer to the stop line of the signalized intersections. Thus, cellphone usage decreases the progression of 13 vehicles per hour due to a reduction in effective green time, increases total delay, and deteriorates the level of service. The study can assist transportation and traffic officials to optimize signal operation to mitigate the impact of cellphone usage and improve urban transportation.

A Novel Family of Exact Nonlinear Cascade Control Design Solutions for a Class of UAV Systems

In this work, a novel family of exact nonlinear control laws is developed for trajectory tracking of unmanned aerial vehicles. The proposed methodology exploits the cascade structure of the dynamic equations of most of these systems. In a first step, the vehicle position in Cartesian coordinates is controlled by means of fictitious inputs corresponding to the angular coordinates, which are fixed to a combination of computed torque and proportional-derivative elements. In a second step, the angular coordinates are controlled as to drive them to the desired fictitious inputs necessary for the first part, resulting in a double-integrator 3-input cascade control scheme. The proposal is put at test in two examples: 4-rotor and 8-rotor aircrafts. Numerical simulations of both plants illustrate the effectiveness of the proposed method, while real-time results of the first one confirm its applicability.

Lane-Level Map-Aiding Approach Based on Non-Lane-Level Digital Map Data in Road Transport Security

To prevent terror attacks in which trucks are used as weapons as happened in Nice or Berlin in 2016, the European Project Autonomous Emergency Maneuvering and Movement Monitoring for Road Transport Security (TransSec) was launched in 2018. One crucial point of this project is the development of a map-aiding approach for the localization of vehicles on digital maps, so that the information in digital map data can be used to detect prohibited driving maneuvers, such as off-road or wrong-way drivers. For example, a lane-level map-aiding approach is required for wrong-way driver detection. Navigation Data Standard (NDS) is one of the worldwide map standards developed by several automobile manufacturers. So far, there is no lane-level NDS map covers a large area, therefore, it was decided to use the latest available NDS map without lane level accuracy. In this paper, a lane-level map-aiding approach based on a non-lane-level NDS map is presented. Due to the inaccuracy of vehicle position and digital map the map-aiding does not always provide the correct results, so probabilities of off-road and wrong-way diver detection are estimated to support risk estimation. The performance of the developed map-aiding approach is comprehensively evaluated with both real and simulated trajectories.

Understanding the Lateral Dimension of Traffic: Measuring and Modeling Lane Discipline

There is growing interest in understanding the lateral dimension of traffic. This trend has been motivated by the detection of phenomena unexplained by traditional models and the emergence of new technologies. Previous attempts to address this dimension have focused on lane-changing and non-lane-based traffic. The literature on vehicles keeping their lanes has generally been limited to simple statistics on vehicle position while models assume vehicles stay perfectly centered. Previously the author developed a two-dimensional traffic model aiming to capture such behavior qualitatively. Still pending is a deeper, more accurate comprehension and modeling of the relationships between variables in both axes. The present paper is based on the Next Generation SIMulation (NGSIM) datasets. It was found that lateral position is highly dependent on the longitudinal position, a phenomenon consistent with data capture from multiple cameras. A methodology is proposed to alleviate this problem. It was also discovered that the standard deviation of lateral velocity grows with longitudinal velocity and that the average lateral position varies with longitudinal velocity by up to 8 cm, possibly reflecting greater caution in overtaking. Random walk models were proposed and calibrated to reproduce some of the characteristics measured. It was determined that drivers’ response is much more sensitive to the lateral velocity than to position. These results provide a basis for further advances in understanding the lateral dimension. It is hoped that such comprehension will facilitate the design of autonomous vehicle algorithms that are friendlier to both passengers and the occupants of surrounding vehicles.

Mechanical Behavior of the Reinforced Retaining Wall under Vehicle Load

This study primarily aims to explore the mechanical behavior and influence factors of the reinforced retaining wall subject to vehicle loads. Mohr–Coulomb model was adopted to simulate and analyze the structural characteristics of the reinforced retaining wall by the finite element method. Its mechanical behavior was investigated in accordance with relevant theories. The results showed that the vertical and horizontal maximum displacement of the reinforced retaining wall occurs at the wall surface of the retaining wall, the maximum internal soil pressure appears at the middle and lower part of the retaining wall, and the maximum tensile strain of the tension bar acts on the wall rupture surface. As impacted by static vehicle load, the largest settlement is located at the parking position, and the maximum horizontal displacement and wall stability will vary with the vehicle position. Moreover, the closer the vehicle to the reinforcement is, the greater the lateral Earth pressure will be imposed on the upper part of the reinforcement body. With the variation of the vehicle position, the tension stress of the geogrid will vary noticeably.

Automatic Vehicle Accident Indication and Reporting System for Road Ways Using Internet of Things

In India, transport becomes a basic commodity of daily life. As transportation starts increasing, safety has become a major concern for consumers. This paper mainly aims at reducing the fatalities caused due to accidents occurring in roadways. In general, many lives could be saved if emergency service could get accurate accident location and rescue the injured people at the minimum possible time. The Internet of Things has revlontinsed the modern world in recent times. As Global Positioning System has become an integral part of any vehicle system, this effective method is utilized to monitor the location of vehicles and send accident locations to an Accidents Monitoring and Rescue Services Centre (AMRSC) using GSM. The Accelerometer located in the vehicle system gives the live status of the vehicle position while the vehicle is in motion. Whenever an accident occurs, the signal from the accelerometer is fed to the controller. The Node MCU controller is programmed to check whether the accident has occurred and given the information to the user and AMRSC as soon as possible. Now, the system will also send the accident location acquired from the GPS along with the vehicle details through the GSM network to AMRSC. After receiving the alert message from the infected user vehicle system, the rescue team will reach the accident location as soon as possible by reading the data from the server.

Vehicle Position Tracking Using Kalman Filter And CWLS Optimization

Vehicle position estimation for wireless network has been studied in many fields since it has the ability to provide a variety of services, such as detecting oncoming collisions and providing warning signals to alert the driver. The services provided are often based on collaboration among vehicles that are equipped with relatively simple motion sensors and GPS units. Awareness of its precise position is vital to every vehicle, so that it can provide accurate data to its peers. Currently, typical positioning techniques integrate GPS receiver data and measurements of the vehicles motion. However, when the vehicle passes through an environment that creates multipath effect, these techniques fail to produce high position accuracy that they attain in open environments. Unfortunately, vehicles often travel in environments that cause multipath effect, such as areas with high buildings, trees, or tunnels. The goal of this research is to minimize the multipath effect with respect to the position accuracy of vehicles. The proposed technique first detects whether there is disturbance in the vehicle position estimate that is caused by the multipath effect using hypothesis test. This technique integrates all information with the vehicle's own data and the Constrained Weighted Least Squares (CWLS) optimization approach with time difference of arrival (TDOA) technique and minimizes the position estimate error of the vehicle. Kalman filter is used for smoothing range data and mitigating the NLOS errors. The positioning problem is formulated in a state-space framework and the constraints on system states are considered explicitly. The proposed recursive positioning algorithm will be comparatively more robust to measurement errors because it updates the technique that feeds the position corrections back to the Kalman Filter as compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements. It compensates the GPS data and decreases random error influence to the position precision. The new techniques presented in this thesis decrease the error in the position estimate. Simulation results show that the proposed tracking algorithm can improve the accuracy significantly.