Interactive Trajectory Prediction for Autonomous Driving via Recurrent Meta Induction Neural Network

Interactive driving is challenging but essential for autonomous cars in dense traffic or urban areas. Proper interaction requires understanding and prediction of future trajectories of all neighboring cars around a target vehicle. Current solutions typically assume a certain distribution or stochastic process to approximate human-driven cars' behaviors. To relax this assumption, a Recurrent Meta Induction Network (RMIN) framework is developed. The original Conditional Neural Process (CNP) on which this is based does not consider the sequence of the conditions, due to the permutation invariance requirements for stochastic processes. However, the sequential information is important for the driving behavior estimation. Therefore, in the proposed method, a recurrent neural cell replaces the original demonstration sub-net. The behavior estimation is conditioned on the historical observations for all related cars, including the target car and its surrounding cars. The method is applied to predict the lane change trajectory of a target car in dense traffic areas. The proposed method achieves better results than previous methods and thanks to the meta-learning framework, it can use a smaller dataset, putting fewer demands on autonomous driving data collection.