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Predicting the future location of vehicles is essential for safety-criticalapplications such as advanced driver assistance systems (ADAS) and autonomousdriving. This paper introduces a novel approach to simultaneously predict boththe location and scale of target vehicles in the first-person (egocentric) viewof an ego-vehicle. We present a multi-stream recurrent neural network (RNN)encoder-decoder model that separately captures both object location and scaleand pixel-level observations for future vehicle localization. We show thatincorporating dense optical flow improves prediction results significantlysince it captures information about motion as well as appearance change. Wealso find that explicitly modeling future motion of the ego-vehicle improvesthe prediction accuracy, which could be especially beneficial in intelligentand automated vehicles that have motion planning capability. To evaluate theperformance of our approach, we present a new dataset of first-person videoscollected from a variety of scenarios at road intersections, which areparticularly challenging moments for prediction because vehicle trajectoriesare diverse and dynamic.

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems