Pedestrian Intention Prediction: A Multi-task Perspective
This work addresses safety for pedestrians in autonomous driving, but it is incremental as it builds on existing multi-task approaches with added visual state details.
The paper tackles pedestrian intention prediction for autonomous vehicles by jointly predicting intention and visual states, including bounding boxes, using a multi-task recurrent neural network. Experiments on the JAAD dataset show superior intention prediction performance and comparable bounding box prediction with a simpler, faster architecture (over 2 times faster).
In order to be globally deployed, autonomous cars must guarantee the safety of pedestrians. This is the reason why forecasting pedestrians' intentions sufficiently in advance is one of the most critical and challenging tasks for autonomous vehicles. This work tries to solve this problem by jointly predicting the intention and visual states of pedestrians. In terms of visual states, whereas previous work focused on x-y coordinates, we will also predict the size and indeed the whole bounding box of the pedestrian. The method is a recurrent neural network in a multi-task learning approach. It has one head that predicts the intention of the pedestrian for each one of its future position and another one predicting the visual states of the pedestrian. Experiments on the JAAD dataset show the superiority of the performance of our method compared to previous works for intention prediction. Also, although its simple architecture (more than 2 times faster), the performance of the bounding box prediction is comparable to the ones yielded by much more complex architectures. Our code is available online.