Pragmatic overview of surrounding traffic implementation into driving simulator

Matowicki, Michal; Přibyl, Ondřej; Bouchner, Petr

doi:10.1109/elektro.2016.7512111

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…The scenarios are created by varying the traffic, weather, and events in that environment. As described in [22], there are different methods to generate driving scenarios and pre-defined trajectory of virtual vehicles in order to simulate traffic. The trajectory of those vehicles can be developed simply based on road geometry, imported data from other simulations, or through an interface that interacts with the driving simulator engine.…”

Section: B Scenario Designmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

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Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

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Section: B Scenario Designmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

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“…Driving simulator technology is one of the most sophisticated applications of computer-aided kinematic and dynamic simulations [ 1 ] designed to simulate the various physical and visual characteristics of driving [ 2 ] and widely employed to explore diverse aspects of the transportation landscape [ 3 ]. For instance, simulators can play a significant role in testing future innovations such as Autonomous Vehicles (AVs) [ 4 ] and evaluating performance as well as passenger experience [ 5 ] in different driving scenarios (i.e., road conditions, weather, traffic [ 6 ], vulnerable road users [ 7 , 8 ]) which is crucial for the development and validation of these technologies before deploying them in real-world use [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Fidelity Assessment of Motion Platform Cueing: Comparison of Driving Behavior under Various Motion Levels

Hamdani

Bouchner

Kunclova

et al. 2023

Sensors

Self Cite

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The present paper focuses on vehicle simulator fidelity, particularly the effect of motion cues intensity on driver performance. The 6-DOF motion platform was used in the experiment; however, we mainly focused on one characteristic of driving behavior. The braking performance of 24 participants in a car simulator was recorded and analyzed. The experiment scenario was composed of acceleration to 120 km/h followed by smooth deceleration to a stop line with prior warning signs at distances of 240, 160, and 80 m to the finish line. To assess the effect of the motion cues, each driver performed the run three times with different motion platform settings–no motion, moderate motion, and maximal possible response and range. The results from the driving simulator were compared with data acquired in an equivalent driving scenario performed in real conditions on a polygon track and taken as reference data. The driving simulator and real car accelerations were recorded using the Xsens MTi-G sensor. The outcomes confirmed the hypothesis that driving with a higher level of motion cues in the driving simulator brought more natural braking behavior of the experimental drivers, better correlated with the real car driving test data, although exceptions were found.

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“…There are many types of them. They are focused on investigating the driver behavior in fatique [1][2][3], testing systems for predicting microsleeps [4], humanmachine interaction [5], interaction with surrounding traffic [6,7] and many more. During their performing the data are collected, which are mostly of a technical nature, and on the other hand, they give us information about the state of the monitored driver.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Stored Data Help to Propose and Generate New Tracks

Jirkovský¹

2017

APP

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Abstract. During experiments on vehicle simulators a large amount of data is stored. On these data, it is possible to trace some similarities in the behavior of drivers in certain areas or when performing the same task. We can assume that if the driver performs a certain type of experiment, his behavior exhibits certain traits. These elements of common behavior can be used to create virtual track for experiments. Which elements and how they can be used is described in this article. The algorithm for automatic creation of virtual track based on type of experiment is provided. It will help us to define the purpose of measurement and the track could be generated automatically.

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Pragmatic overview of surrounding traffic implementation into driving simulator

Cited by 6 publications

References 8 publications

A Review of Driving Simulation Technology and Applications

A Review of Driving Simulation Technology and Applications

Fidelity Assessment of Motion Platform Cueing: Comparison of Driving Behavior under Various Motion Levels

Analysis of Stored Data Help to Propose and Generate New Tracks

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