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In order to be globally deployed, autonomous cars must guarantee the safetyof pedestrians. This is the reason why forecasting pedestrians' intentionssufficiently in advance is one of the most critical and challenging tasks forautonomous vehicles. This work tries to solve this problem by jointlypredicting the intention and visual states of pedestrians. In terms of visualstates, whereas previous work focused on x-y coordinates, we will also predictthe size and indeed the whole bounding box of the pedestrian. The method is arecurrent neural network in a multi-task learning approach. It has one headthat predicts the intention of the pedestrian for each one of its futureposition and another one predicting the visual states of the pedestrian.Experiments on the JAAD dataset show the superiority of the performance of ourmethod compared to previous works for intention prediction. Also, although itssimple architecture (more than 2 times faster), the performance of the boundingbox prediction is comparable to the ones yielded by much more complexarchitectures. Our code is available online.

Pedestrian Intention Prediction: A Multi-task Perspective