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For human drivers, having rear and side-view mirrors is vital for safedriving. They deliver a more complete view of what is happening around the car.Human drivers also heavily exploit their mental map for navigation.Nonetheless, several methods have been published that learn driving models withonly a front-facing camera and without a route planner. This lack ofinformation renders the self-driving task quite intractable. We investigate theproblem in a more realistic setting, which consists of a surround-view camerasystem with eight cameras, a route planner, and a CAN bus reader. Inparticular, we develop a sensor setup that provides data for a 360-degree viewof the area surrounding the vehicle, the driving route to the destination, andlow-level driving maneuvers (e.g. steering angle and speed) by human drivers.With such a sensor setup we collect a new driving dataset, covering diversedriving scenarios and varying weather/illumination conditions. Finally, welearn a novel driving model by integrating information from the surround-viewcameras and the route planner. Two route planners are exploited: 1) byrepresenting the planned routes on OpenStreetMap as a stack of GPS coordinates,and 2) by rendering the planned routes on TomTom Go Mobile and recording theprogression into a video. Our experiments show that: 1) 360-degreesurround-view cameras help avoid failures made with a single front-view camera,in particular for city driving and intersection scenarios; and 2) routeplanners help the driving task significantly, especially for steering angleprediction.

End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners