Predicting Lane Change Decision Making with Compact Support

In the foreseeable future, autonomous vehicles will have to drive alongside human drivers. In the absence of vehicle-to-vehicle communication, they will have to be able to predict the other road users' intentions. Moreover, they will also need to behave like a typical human driver so that other road users can infer their actions. It is critical to be able to learn a human driver's mental model and integrate it into the Planning & Control algorithm. In this paper, we present a robust method to predict lane changes as cooperative or adversarial. For that, we first introduce a method to extract and annotate lane changes as cooperative and adversarial based on the entire lane change trajectory. We then propose to train a specially designed neural network to predict the lane change label before the lane change has occurred and quantify the prediction uncertainty. The model will make lane change decisions following human drivers' driving habits and preferences, i.e., it will only change lanes when the surrounding traffic is considered to be appropriate for the majority of human drivers. It will also recognize unseen novel samples and output low prediction confidence correspondingly to alert the driver to take control in such cases. We published the lane change dataset and codes at https://github.com/huanghua1668/1c_csnn.