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Antonio Lopez, David Vazquez, & Gabriel Villalonga. (2018). Data for Training Models, Domain Adaptation. In Intelligent Vehicles. Enabling Technologies and Future Developments (pp. 395–436).
Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection.
Data for Training Models, Domain AdaptationAntonio LopezDavid VazquezGabriel Villalongaopenurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2F158.109.8.37%2F&genre=bookitem&atitle=Data%20for%20Training%20Models%2C%20Domain%20Adaptation&title=Intelligent%20Vehicles.%20%20Enabling%20Technologies%20and%20Future%20Developments&btitle=Intelligent%20Vehicles.%20%20Enabling%20Technologies%20and%20Future%20Developments&date=2018&spage=395%E2%80%93436&aulast=Antonio%20Lopez&au=David%20Vazquez&au=Gabriel%20Villalonga&id=https%3A%2F%2Fdoi.org%2F10.1016%2FB978-0-12-812800-8.00010-2&sid=refbase%3ACVCAntonio Lopez, David Vazquez, & Gabriel Villalonga. (2018). Data for Training Models, Domain Adaptation. In Intelligent Vehicles. Enabling Technologies and Future Developments (pp. 395-436).2018BookChaptertextDriving simulatorhardwaresoftwareinterfacetraffic simulationmacroscopic simulationmicroscopic simulationvirtual datatraining dataurl:https://doi.org/10.1016/B978-0-12-812800-8.00010-2Intelligent Vehicles. Enabling Technologies and Future Developments2018395436