Estimation of Vehicle Longitudinal Speed Based on Improved Kalman Filter

Estimation of vehicle longitudinal acceleration is very important in vehicle active safety control system. In this paper, two driving conditions of a 4WD off-road vehicle are divided by vehicle signals such as steering angle. Under different working conditions, different estimation algorithms are adopted. In the straight driving condition, the longitudinal speed was estimated by adjusting the variance weight of acceleration Kalman observation noise based on kinematics method. For steering conditions, in order to obtain the longitudinal velocity at the center of mass, by dynamic method, a lateral state estimator was designed and tire sideslip dynamics was modeled. The CarSim-Simulink co-simulation results show that the proposed algorithm has high accuracy and strong practicability.

Investigating the Performance of a Laboratory Dynamometer for a Three-wheeler Engine Test Rig

The dynamometer is to mimics driving condition in an indoor controlled condition measuring engine output parameters like speed, torque, power, combustion conditions and exhaust compositions. However, the complexity and dynamics involved in the making of this instrument makes it unaffordable for student research except it is funded. This work reports an innovative method adopted to measure a three-wheeler engine output parameters. An automobile vehicle dynamo was adopted which requires low speed and outputs high torque. Driving conditions were incorporated as brakes for the dynamo which mimics the driving terrains experienced by the engine. A data acquisition unit capable of converting the electrical signals into readable values on a screen and a user interface on a PC completed the dynamometer development. The dynamometer was then calibrated against known output values of speed and torque. A three-wheeler engine was mounted on a test rig and the dynamometer was used to measure output speed, torque and energy dissipated. A Bajaj engine was mounted and the engine output tested for two types of transmission of manual and continuously variable transmission. The measured output from the engine showed that the developed dynamometer performed effectively and therefore be adopted for use for student research in this area. Keywords— dynamometer, load, speed, terrain, torque.

Effects of unreliable automation and takeover time budget on young drivers’ mental workload

Young drivers are involved in a significantly higher number of crashes than any other age group. Highly automated vehicles are expected to improve traffic safety and reduce human errors; however, driver needs to take over in situations where the automation fails. A driving simulation study was conducted with twenty-eight young drivers to investigate the effects of unreliable automation, different takeover time budgets (TOTBs), and secondary tasks on mental workload during takeover situations. Mental workload was measured using a combination of physiological and secondary task performance measures. Results suggested that when faced with critical incidents, young drivers experienced lower mental workload under 10s of TOTB as compared to 5s or 8s of TOTB especially when they were engaged in a demanding secondary task. In addition, drivers experienced higher mental workload when regaining the control of the vehicle in the automated mode than the manual driving condition.

Exploring traffic safety climate with driving condition and driving behaviour: a random parameter structural equation model approach

This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour. To achieve the objective, the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features, vehicle profiles, roadway conditions and environment conditions. The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016, including 27 arterials with 16 827 injury samples. By quantifying the driving conditions and driving actions, the random parameter structural equation model was built up with measurement variables and latent variables. Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively, while driving conditions and driving actions were quantified and reflected by vehicles, road environment and crash features correspondingly. The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.

Study on the Stability Control of Vehicle Tire Blowout Based on Run-Flat Tire

In order to study the stability of a vehicle with inserts supporting run-flat tires after blowout, a run-flat tire model suitable for the conditions of a blowout tire was established. The unsteady nonlinear tire foundation model was constructed using Simulink, and the model was modified according to the discrete curve of tire mechanical properties under steady conditions. The improved tire blowout model was imported into the Carsim vehicle model to complete the construction of the co-simulation platform. CarSim was used to simulate the tire blowout of front and rear wheels under straight driving condition, and the control strategy of differential braking was adopted. The results show that the improved run-flat tire model can be applied to tire blowout simulation, and the performance of inserts supporting run-flat tires is superior to that of normal tires after tire blowout. This study has reference significance for run-flat tire performance optimization.

An Intelligent Mobile App to Detect Drowsy Driving with Artificial Intelligence

Drowsy driving is lethal- 793 died from accidents related to drowsy driving and 91000 accidents related to drowsy driving occurred [1]. However, drowsy driving and accidents related to drowsy driving are preventable. In this paper, we address the problem through an application that uses artificial intelligence to detect the eye openness of the user. The application can detect the eyes of the user via computer vision. Based on the user’s eye openness and frequencies, the sleepy driving condition can be inferred by this application. We applied our application to actual driving environments on the highway, both day and night, as well as within a normal control situation using a qualitative evaluation approach. The result shows that it is 88% effective during the day and 75% effective during nighttime. This result reveals effectiveness and accuracy of detection during daytime application under controlled testing, which is more flexible and efficient comparing to previous works. Effectiveness and accuracy for nighttime detection and detections with the presence of other distractions can be further improved.