Driver Simulator 2020: Test Your Driving Skills in Different Modes and Challenges
This is an interactive truck driving simulator game. Drive a truck loaded up with cargo and transport it to the given point. Can you complete the missions like a pro?Release DateSeptember 2018 (Android). March 2020 (WebGL).
Objective: The National Highway Traffic Safety Administration in the USA estimated that the effects of drowsiness while driving led to approximately 72,000 crashes, 44,000 injuries, and 800 deaths in 2013. Keeping this in mind, the risk and injuries of drowsy driving remain a major safety issue that clearly needs to be studied. Our purpose was to conduct a systematic review of international literature including studies on driving behavior associated to drowsy and fatigued drivers. The research focused on the prediction and effects of drowsiness, and particularly on studies based on driving in simulated environments. Additionally, we searched for studies related to driving simulators, in general, to better understand the tool's efficacy and its advantages and disadvantages.Methods: This review was made in accordance with PRISMA statement guidelines. After conducting in-depth research in targeted databases, 23 studies met the inclusion criteria; the papers were analyzed regarding the type of experiment and procedures and driving performance of 690 participants was studied.Results: Studies revealed that drowsiness have effects on driving performance and these effects become more relevant with time-on-task and in monotonous scenarios and landscapes. In addition, some documents include validations of several technologies to detect and predict sleepiness.Conclusions: Overall, we can conclude that drowsiness and fatigue impair driving performance, resulting in drivers who are more exposed to risky situations.
driver simulator 2020
Attendees at the Consumer Electronics Show, or CES, got to try out cooperative merging technology created at the Center for Environmental Research and Technology, or CE-CERT, using a high-fidelity driving simulator.
Peng Hao and Guoyuan Wu, research faculty members at CE-CERT, solved this problem with technology that allows cars to communicate with each other and subsequently recommend speeds that create a gap between mainline vehicles so the merging vehicle can slide in. When done right, drivers can improve fuel consumption by approximately 10%-40% and reduce emissions dramatically.
Munich. The BMW Group is creating every opportunity for its vehicle research and development engineers to simulate and test the product requirements of the future under realistic conditions with its new Driving Simulation Centre. With 14 simulators and usability labs covering an area of 11,400 square metres, it is the most advanced and diversified simulation centre in the automotive industry.
Thanks to an installation concept featuring an ingenious transportation and docking system, all the simulators can be used on the same day with different vehicle models if required. The centre thereby offers a high level of flexibility for all specialist areas of development, while also enabling maximum utilisation of capacity.
High-tech on an impressive scale: the high-fidelity and high-dynamic simulators. The high-fidelity and high-dynamic simulators are the standout highlights of the new Driving Simulation Centre, both visually and technologically. They create the type of test conditions that in the past could only be experienced with actual test vehicles on the road. Besides targeted optimisation of innovative user functions, testing in the lab has the added benefit of making it possible to reproduce specific driving situations as often as required, significantly increasing the validity of the evaluated test results. The driving simulators can also be used for acting out test scenarios that seldom occur in real-life driving and only under unusual circumstances, or that involve an element of danger and therefore cannot be recreated for test purposes alone out on an actual road. However, findings from on-road testing can be checked and validated by means of realistic simulations in the lab.
The new high-dynamic simulator is capable of generating longitudinal and lateral acceleration forces of up to 1.0 g. It replicates highly dynamic evasive action, emergency braking and hard acceleration when testing out new systems and functions.
The longitudinal and lateral movements of both simulators are produced using a sophisticated system of wheels and rails, which reacts virtually instantaneously to driver inputs such as steering commands. This allows all the characteristic nuances of driving pleasure in a BMW to be experienced in the simulator. This is achieved by using linear electric motors with no moving parts. In order to generate the necessary forces, these electric motors hover above a series of magnets with poles alternating in quick succession, similar to the magnetic levitation technology found in high-speed maglev trains. Supercapacitors deliver the peak power required by the motion system in fractions of a second, with the motion system then recuperating energy by means of regenerative braking and feeding it back to the supercapacitors.
The tests take place inside a platform of the driving simulator with a distinctive dome shape. Here, the systems for testing are installed in a vehicle mock-up. The dome is mounted on an electromechanical hexapod system and can be moved in both a longitudinal and a lateral direction by means of a further electric drive unit. Inside the dome, the vehicle mock-up stands on a turntable for recreating rotary movements.
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The Department of Industrial and Systems Engineering at Texas A&M University installed a new driving simulator to use in research pertaining to driving, autonomous vehicles and other vehicle technologies.
Here are the top three things you need to know about the simulator, including what types of research is currently being done and future areas of research that will help increase safety on the road, including a future with self-driving vehicles.
The state-of-the-art simulator features a 270-degree field of vision, which provides a realistic driving experience for the user. Field of vision is the area you can see on each side, your peripheral vision, while you look straight ahead. This is a very rare field of vision for a simulator and there are only a few simulators with this capability in the United States.
The Human Factors and Machine Learning Laboratory is using the simulator for research on autonomous vehicles and cyclist safety, in partnership with researchers in the Department of Landscape Architecture and Department of Psychological and Brain Sciences. This research looks at how bias may play a role in cyclist interactions with vehicles. Realistic driving scenarios were created for the simulator that allowed the researchers to measure the impact of bias on driver and cyclist interactions. This work was funded by the T3 grants awarded through the Office of the President.
Many different types of research can be done on the driving simulator including research on drowsy drivers, autonomous vehicles, on-road sign evaluation, driving education, driver behavior and much more.
Become a school bus driver. You have a difficult job ahead of you. Every day drive the wailing children, watch them not to destroy anything. But you just love driving large vehicles. Your most favorite part of the work is parking in the depot. Sometimes it's not difficult at all, sometimes you have to go through the other buses. Most of the time, your bus is dirty, so you have to go to the car wash. Show how much you love the vehicles and play School Bus Driving Simulator 2020, where you can show everyone how precise you can park. Have fun.
Background: Stress measured in simulation scenarios could thus far show an overall change in the stress state, but not be well attributed to acute stressful events. Driving simulator scenarios that induce stress measurable at the event level in realistic situations are thus warranted. As such, acute stress reactions can be measured in the context of changing situational factors such as fatigue, substance abuse, or medical conditions.
Method: Twelve healthy female participants drove the same route numerous times in a driving simulator, each time with different random traffic events occurring throughout. During one of the scenarios, unknown to the participants, 10 programmed neutral traffic events occurred, whereas in another scenario, at the same location, 10 stressful events occurred.
Application: The developed simulator scenarios enable us to measure stress reactions in driving situations at the time when the event actually happens. With these scenarios, we can measure how situational factors, such as fatigue or substance abuse, can change immediate stress reactions when driving. We can further measure more specifically how induced driving stress can affect physical and mental functioning afterward.
Hands-free conversation with simple content (HFS): the cell phone was placed on the dashboard and drivers can hear through a loudspeaker without using a hand to hold it. Drivers were asked to answer problems of single-digit addition and subtraction; for example, what is the answer to two and five?
One goal of the study was to investigate whether the model proposed in Engström et al.  could be implemented on different levels of cognitive load. A driving simulator experiment with three phone conversation modes (baseline, hands-free, and handheld) and two conversation contents (simple and complex) was designed to produce different levels of cognitive load. Results in Section 3.1 and Section 3.2 indicated that the previously proposed stochastic mechanism model fitted well on different levels of cognitive load. The two measures adopted in the accumulator model to quantify visual looming, and , both increase as the threat draws nearer. Although the two measures have been adopted in many previous studies [38, 39, 40], few of them compared the two measures and drew a conclusion on the efficiency of the two measures in quantify looming. In both of the two braking scenarios designed in this study, accumulator model fitted better by using (see AIC values listed in Tables 3 and 4). Although in the first braking scenario, a smaller AIC was obtained for model fitted with , the AIC between the two models was in the range of four to seven, indicating that the model could be plausible as well [41, 42]. In this way, the results generated here were consistent with the findings in Xue et al. , which suggested that the visual looming information drivers adopted to take a brake response was more similar to .