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dc.contributor.authorMueller, Matthias
dc.contributor.authorDosovitskiy, Alexey
dc.contributor.authorGhanem, Bernard
dc.contributor.authorKoltun, Vladlen
dc.date.accessioned2019-06-26T11:38:16Z
dc.date.available2019-06-26T11:38:16Z
dc.date.issued2019-01-13
dc.identifier.urihttp://hdl.handle.net/10754/655718
dc.description.abstractDriving Policy Transfer via Modularity and Abstraction Summary ➢We transfer driving policies from simulation to reality via modularity and abstraction. ➢The driving policy is encapsulated such that it is not directly exposed to raw perceptual input or low-level vehicle dynamics. ➢We evaluate our approach in simulated urban environments and in various real-world conditions in two different continents. Simulation ➢We use CARLA, an open-source simulator for urban driving. ➢The simulator provides access to sensor data from the ego-vehicle, as well as detailed privileged information about the ego-vehicle and the environment. ➢CARLA provides access to two towns: Town 1 and Town 2 which differ in their layout, size, and visual style. ➢CARLA also provides multiple environmental conditions (combinations of weather and lighting). ➢We use two of these in our experiments: clear daytime and cloudy daytime after rain. ➢The two towns and environmental conditions used in our experiments are illustrated on the right. Physical World ➢We use a modified 1/5 scale Traxxas Maxx truck as vehicle. ➢At runtime, given an image, the onboard computer predicts the waypoints and uses a PID controller to convert them to low-level control commands. ➢While the car is driving, the driving policy can be guided by high-level command inputs through a switch on the remote control.
dc.relation.urlhttps://epostersonline.com/wep2019/node/67
dc.titleDriving Policy Transfer via Modularity and Abstraction
dc.typePoster
dc.conference.dateJANUARY 13 - 17 , 2019
dc.conference.nameWEP Library ePoster competition 2019
dc.conference.locationKAUST
dc.contributor.institutionDalhousie University
dc.contributor.institutionIntel Labs
refterms.dateFOA2019-06-26T11:38:16Z


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