Development and Validation of a Vehicle Dynamics Integrated Traffic Simulation Environment Assessing Surrogate Safety

2015 ◽  
Vol 29 (5) ◽  
pp. 04014080 ◽  
Author(s):  
Jaehyun (Jason) So ◽  
Byungkyu (Brian) Park ◽  
Sage M. Wolfe ◽  
George Dedes
Keyword(s):  
Vehicle Dynamics ◽  
Traffic Simulation ◽  
Simulation Environment ◽  
Development And Validation

Motion Simulation Experiments for Driver Behavior and Road Vehicle Dynamics

2011 ◽  
Vol 11 (4) ◽  
Author(s):  
Kemper Lewis ◽  
Kevin Hulme ◽  
Edward Kasprzak ◽  
Deborah Moore-Russo ◽  
Gregory Fabiano
Keyword(s):  
Vehicle Dynamics ◽  
Large Scale ◽  
Driving Simulation ◽  
Vehicle Design ◽  
Simulation Environment ◽  
Learning Mechanisms ◽  
Road Vehicle ◽  
Wide Range ◽  
Traffic Systems ◽  
The Impact

This paper discusses the design and development of a motion-based driving simulation and its integration into driving simulation research. The integration of the simulation environment into a road vehicle dynamics curriculum is also presented. The simulation environment provides an immersive experience to conduct a wide range of research on driving behavior, vehicle design and intelligent traffic systems. From an education perspective, the environment is designed to promote hands-on student participation in real-world engineering experiences that enhance conventional learning mechanisms for road vehicle dynamics and engineering systems analysis. The paper assesses the impact of the environment on student learning objectives in an upper level vehicle dynamics course and presents results from research involving teenage drivers. The paper presents an integrated framework for the use of real-time simulation and large-scale visualization to both study driving behaviors and to discover the impact that design decisions have on vehicle design using a realistic simulated driving interface.

scholarly journals Testing an Adaptive Cruise Controller with coupled traffic and driving simulations

10.29007/84rc ◽  
2019 ◽  
Author(s):  
Mirko Barthauer ◽  
Alexander Hafner
Keyword(s):  
Traffic Flow ◽  
Driving Simulator ◽  
Traffic Simulation ◽  
Traffic Signals ◽  
Simulation Environment ◽  
Cruise Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Subjective Judgement ◽  
Traffic Simulations

In many cases, driving simulator studies target how test persons interact with surround- ing traffic and with traffic signals. Traffic simulations like SUMO specialize in modeling traffic flow, which includes signal control. Consequently, driving and traffic simulation are coupled to benefit from the advantages of both. This means that all except the driven (ego) vehicle are controlled by the traffic simulation. Essential vehicle dynamics data are exchanged and applied frequently to make the test person interact with SUMO-generated traffic. Additionally, traffic lights are controlled by SUMO and transferred to the driving simulation. The system is used to evaluate an Adaptive Cruise Control (ACC) system, which considers current and future traffic light states. Measures include objective terms like traffic flow as well as the subjective judgement of the signal program, the ACC and the simulation environment.

Comparative Study of Rack Force Estimation for Electric Power Assist Steering System

2017 ◽  
Author(s):  
Yijun Li ◽  
Taehyun Shim ◽  
Dexin Wang ◽  
Timothy Offerle
Keyword(s):  
Control System ◽  
Electric Power ◽  
Vehicle Dynamics ◽  
System Development ◽  
Steering System ◽  
Estimation Methods ◽  
Simulation Environment ◽  
Force Estimation ◽  
Adaptation Algorithm ◽  
Estimation Algorithms

For an electric power assist steering (EPAS) control system, it is important to know the rack force information to improve the steering feel control performance. Since there is no direct measurement of rack force in current EPAS system, there have been various rack force estimation algorithms proposed for the control system development. In this paper, two existing rack force estimation methods (based on steering system dynamics and vehicle dynamics) have been implemented in the simulation environment to compare its performance. The effectiveness and limitations of two methods have been analyzed using a simulation of high fidelity EPAS model with various inputs conditions. In addition, new adaptation algorithm is proposed to further improve the estimation performance of the existing methods.

Validation of a Real-Time Multibody Model for an X-by-Wire Vehicle Prototype Through Field Testing

2015 ◽  
Vol 10 (3) ◽  
Author(s):  
Roland Pastorino ◽  
Emilio Sanjurjo ◽  
Alberto Luaces ◽  
Miguel A. Naya ◽  
Wim Desmet ◽  
...  
Keyword(s):  
Real Time ◽  
Collision Detection ◽  
Vehicle Dynamics ◽  
Field Testing ◽  
Simulation Environment ◽  
Multibody Model ◽  
Graphical Environment ◽  
Road Profile ◽  
Lateral Vehicle Dynamics ◽  
Vehicle Motion

This research focuses on the experimental validation of a real-time vehicle multibody (MB) model whose bodies are considered rigid. For this purpose, a vehicle prototype has been built and automated in order to repeat reference maneuvers. Numerous sensors on the prototype gather the most relevant magnitudes of the vehicle motion. Two low speed maneuvers involving the longitudinal and lateral vehicle dynamics have been repeated multiple times in a test area. Then, a real-time MB model of the vehicle prototype has been self-developed as well as a simulation environment that includes a true graphical environment, a true road profile, and collision detection. Subsystems like brakes and tires have also been modeled. Both test maneuvers have been simulated with the MB model in the simulation environment using inputs measured experimentally. Selected simulation variables have been compared to their experimental counterparts provided with a confidence interval (IC) that characterizes the field testing (FT) process errors. The results of the comparisons show good correlation between simulation predictions and experimental data, thus allowing to extract useful guidelines to build accurate real-time vehicle MB models. In this way, the present work aims to contribute to the scarce literature on vehicle complete validation studies.

SCOOT Real-Time Adaptive Control in a CORSIM Simulation Environment

2000 ◽  
Vol 1727 (1) ◽  
pp. 27-30 ◽  
Author(s):  
Blake G. Hansen ◽  
Peter T. Martin ◽  
H. Joseph Perrin
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Traffic Simulation ◽  
Optimization Technique ◽  
Fixed Time ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Simulation Environment ◽  
Capital Costs ◽  
Adaptive Signal Control

The manner in which the adaptive signal control system SCOOT (Split, Cycle, Offset Optimization Technique) has been connected to the CORSIM traffic simulation model is described. To demonstrate the connection, CORSIM simulates the traffic activity of a six-node traffic network under SCOOT’s adaptive traffic signal control. CORSIM’s “virtual detectors” provide the necessary data for SCOOT optimization in real time. In a completed loop, the optimized signal timing is then communicated from SCOOT to CORSIM, which implements the timing and updates the traffic simulation. This means that SCOOT is now functioning in an entirely simulated environment. A comparison of delay and travel time is presented for a six-intersection street network under SCOOT control and under fixed-time area control optimized with TRANSYT-7F. The results show reductions in delay and numbers of stops of 20 to 30 percent. Previously, the measurement of the benefit of adaptive control has been limited to evaluations of systems after implementation. It is shown how SCOOT can now be evaluated under various network traffic conditions in a simulation environment and tested on a specific city network to evaluate the benefits before capital costs are committed.

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