A novel evaluation method for vehicle and traffic performance of different decision control of automatic lane change based on miniature model

2020 ◽  
Vol 234 (12) ◽  
pp. 2794-2806 ◽  
Author(s):  
Yangyang Wang ◽  
Guangda Chen ◽  
Yuanxing Jiang
Keyword(s):  
Traffic Safety ◽  
Evaluation Method ◽  
Physical Simulation ◽  
Road Traffic ◽  
Driver Model ◽  
Lane Change ◽  
Simulation Test ◽  
Lane Changes ◽  
Decision Control ◽  
Lane Change Test

Research on automatic lane-change decision is mainly limited to simulation validation and lacks real vehicle validation methods because it is limited by experimental site and automatic driving technology on real vehicles. This paper puts forward a miniature traffic model to simulate the actual traffic scene and achieves to verify the decision control of automatic lane-change scene. The miniature intelligent traffic scene contains miniature vehicles, simplified miniature road traffic environment, and wireless network communication. After testing the basic functions of the miniature traffic scene model, such as automatic lane change, lane keeping, and automatic following, a semi-physical simulation test of the traffic flow composed of the model vehicle and the virtual vehicle is carried out. The semi-physical simulation test includes vehicle-following test of hybrid-condition intelligent driver model, lane-change test of lane-change decision two-vehicle gaming model, and minimizing overall braking induced by lane changes. The results show that the feasibility of the method and of the lane-change decision two-vehicle gaming model of automatic lane change is better in terms of traffic safety, traffic efficiency, and homogeneity. Compared to the minimizing overall braking induced by lane-change model test, the test of lane-change decision two-vehicle gaming model improves 2.26% and 1.5% in the average speed and total driving distance, respectively. The standard deviation of the traffic speed of the lane-change decision two-vehicle gaming model was 28.57% lower than the minimizing overall braking induced by lane changes. Compared to pure simulation verification, the method considers the effects of actual sensor signals and actuator control, which is closer to the actual application.

Assessment of Urban Road Traffic Safety Based on Bayes Discriminant Analysis Method

2013 ◽  
Vol 639-640 ◽  
pp. 544-547
Author(s):  
Chang Ping Wen ◽  
Qing Qing Tian
Keyword(s):  
Discriminant Analysis ◽  
Traffic Safety ◽  
Evaluation Method ◽  
Road Traffic ◽  
Urban Road ◽  
Discriminant Model ◽  
Road Traffic Safety ◽  
Normal Populations ◽  
Proposed Model ◽  
Bayes Discriminant Analysis

Bayes discriminant analysis theory (BDAT) is used to create an evaluation method to determine the condition of urban road traffic safety. The resulting Bayes discriminant model (BDM) is designed to strictly adhere to BDAT. Three indexes including death ratio per ten thousand vehicles, death ratio per hundred thousand bicycles and death ratio per hundred thousand citizens are selected as the factors in the analysis of urban road traffic safety. The grade of condition of urban road traffic safety is divided into three grades that are regarded as three normal populations in Bayes discriminant analysis. Bayes discriminant functions rigorously constructed through training a set of samples are employed to compute the Bayes function values of the evaluating samples, and the maximal function value is used to judge which population the evaluating sample belongs to. The optimality of the proposed model is verified by back-substitution method. The study shows that the prediction accuracy of the proposed model is 100% and could be used in practice.

Human Factor Issues Associated with Lane Change Collision Avoidance Systems: Effects of Authority, Control, and Ability on Drivers’ Performance and Situation Awareness

2017 ◽  
Vol 61 (1) ◽  
pp. 1634-1638 ◽  
Author(s):  
Husam Muslim ◽  
Makoto Itoh
Keyword(s):  
Human Factors ◽  
Collision Avoidance ◽  
Situation Awareness ◽  
Traffic Safety ◽  
Force Feedback ◽  
Road Traffic ◽  
Warning System ◽  
Lane Change ◽  
Road Traffic Safety ◽  
Automatic Action

In order to improve road traffic safety, increasingly sophisticated and robust collision avoidance systems are being developed. When employed in safety-critical situations, however, the interaction between the human factors and these systems may increase the complexity of the task of driving. Due to these human factors, the ability of the driver to respond to various traffic dangers is considered to be a function of the level of automation, balance of control authority, and the innate ability of the driver. For the purpose of this study, a driving experiment was designed using two types of lane change collision avoidance systems. One was a haptic warning system that provides a steering force feedback to avoid hazardous lane change, and the other, a semi-autonomous system that provides an automatic action to prevent hazardous lane change. While drivers had the final authority over the haptic system, they were unable to override the automatic action. Both systems were examined in three conditions: i) hazard that can be detected only by the system, ii) hazard that can be detected only by the driver, and iii) combined hazards. The different support systems were applied to the different hazards resulting in significant differences in drivers’ reaction time and steering behavior. The drivers’ subjective post-hazard assessments were significantly affected by the type of encountered hazard.

scholarly journals Bifurcation of Lane Change and Control on Highway for Tractor-Semitrailer under Rainy Weather

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Tao Peng ◽  
Zhiwei Guan ◽  
Ronghui Zhang ◽  
Jinsong Dong ◽  
Kening Li ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Feedback Linearization ◽  
Oscillatory Behavior ◽  
Lane Change ◽  
Lane Changing ◽  
Lane Changes ◽  
Rainy Weather ◽  
Matrix Eigenvalues ◽  
Vehicle Test ◽  
And Control

A new method is proposed for analyzing the nonlinear dynamics and stability in lane changes on highways for tractor-semitrailer under rainy weather. Unlike most of the literature associated with a simulated linear dynamic model for tractor-semitrailers steady steering on dry road, a verified 5DOF mechanical model with nonlinear tire based on vehicle test was used in the lane change simulation on low adhesion coefficient road. According to Jacobian matrix eigenvalues of the vehicle model, bifurcations of steady steering and sinusoidal steering on highways under rainy weather were investigated using a numerical method. Furthermore, based on feedback linearization theory, taking the tractor yaw rate and joint angle as control objects, a feedback linearization controller combined with AFS and DYC was established. The numerical simulation results reveal that Hopf bifurcations are identified in steady and sinusoidal steering conditions, which translate into an oscillatory behavior leading to instability. And simulations of urgent step and single-lane change in high velocity show that the designed controller has good effects on eliminating bifurcations and improving lateral stability of tractor-semitrailer, during lane changing on highway under rainy weather. It is a valuable reference for safety design of tractor-semitrailers to improve the traffic safety with driver-vehicle-road closed-loop system.

scholarly journals Fault Detection Based on Parity Equations in Multiple Lane Road Car-Following Models Using Bayesian Lane Change Estimation

2020 ◽  
Vol 9 (4) ◽  
pp. 52
Author(s):  
Mădălin-Dorin Pop ◽  
Octavian Proștean ◽  
Gabriela Proștean
Keyword(s):  
Fault Detection ◽  
Road Traffic ◽  
Lane Change ◽  
Car Following ◽  
Lane Changes ◽  
Detection Analysis ◽  
Knowing That ◽  
Change Behavior ◽  
Car Following Model ◽  
Network Fault

One of the current topics of interest in transportation science is the use of intelligent computation and IoT (Internet of Things) technologies. Researchers have proposed many approaches using these concepts, but the most widely used concept in road traffic modeling at the microscopic level is the car-following model. Knowing that the standard car-following model is single lane-oriented, the purpose of this paper is to present a fault detection analysis of the extension to a multiple lane car-following model that uses the Bayesian reasoning concept to estimate lane change behavior. After the application of the latter model on real traffic data retrieved from inductive loops placed on a road network, fault detection using parity equations was used. The standard car-following model applied separately for each lane showed the ability to perform a lane change action and to incorporate a new vehicle into the current lane. The results will highlight the advantages and the critical points of influence in the use of a multiple lane car-following model based on probabilistic estimated lane changes. Additionally, this research applied fault detection based on parity equations for the proposed model. The purpose was to deliver an overview of the faults introduced by the behavior of vehicles in adjacent lanes on the behavior of the target vehicle.

scholarly journals COVID Lockdowns, Social Distancing, and Fatal Car Crashes: More Deaths on Hobbesian Highways?

2020 ◽  
Vol 4 (3-4) ◽  
pp. 238-259 ◽  
Author(s):  
Marshall W. Meyer
Keyword(s):  
World War Ii ◽  
Traffic Safety ◽  
Motor Vehicle ◽  
Road Traffic ◽  
Research Question ◽  
Traffic Volume ◽  
Similar Data ◽  
World War ◽  
Social Distancing ◽  
The Usa

Abstract Research Question What happened to US traffic safety during the first US COVID-19 lockdown, and why was the pattern the opposite of that observed in previous sudden declines of traffic volume? Data National and local statistics on US traffic volume, traffic fatalities, injury accidents, speeding violations, running of stop signs, and other indicators of vehicular driving behavior, both in 2020 and in previous US economic recessions affecting the volume of road traffic. Methods Comparative analysis of the similarities and differences between the data for the COVID-19 lockdown in parts of the USA in March 2020 and similar data for the 2008–2009 global economic crisis, as well as other US cases of major reductions in traffic volume. Findings The volume of traffic contracted sharply once a COVID-19 national emergency was declared and most states issued stay-at-home orders, but motor vehicle fatality rates, injury accidents, and speeding violations went up, and remained elevated even as traffic began returning toward normal. This pattern does not fit post-World War II recessions where fatality rates declined with the volume of traffic nor does the 2020 pattern match the pattern during World War II when traffic dropped substantially with little change in motor vehicle fatality rates. Conclusions The findings are consistent with a theory of social distancing on highways undermining compliance with social norms, a social cost of COVID which, if not corrected, poses potential long-term increases in non-compliance and dangerous driving.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

scholarly journals Assessing inequality, irregularity, and severity regarding road traffic safety during COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Lin ◽  
Feng Shi ◽  
Weizi Li
Keyword(s):  
New York ◽  
Los Angeles ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Human Mobility ◽  
Critical Conditions ◽  
Road Traffic Safety ◽  
Accident Data ◽  
The Impact

AbstractCOVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for “Hispanic” and “Male” groups; (2) the “hot spots” of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.

Sign in / Sign up

Export Citation Format

Share Document