Relational Recurrent Neural Networks For Vehicle Trajectory Prediction

International audienceScene understanding and future motion prediction of surrounding vehicles are crucial to achieve safe and reliable decision-making and motion planning for autonomous driving in a highway environment. This is a challenging task considering the correlation between the drivers behaviors. Knowing the performance of Long Short Term Memories (LSTMs) in sequence modeling and the power of attention mechanism to capture long range dependencies, we bring relational recurrent neural networks (RRNNs) to tackle the vehicle motion prediction problem. We propose an RRNNs based encoder-decoder architecture where the encoder analyzes the patterns underlying in the past trajectories and the decoder generates the future trajectory sequence. The originality of this network is that it combines the advantages of the LSTM blocks in representing the temporal evolution of trajectories and the attention mechanism to model the relative interactions between vehicles. This paper compares the proposed approach with the LSTM encoder decoder using the new large scaled naturalistic driving highD dataset. The proposed method outperforms LSTM encoder decoder in terms of RMSE values of the predicted trajectories. It outputs an estimate of future trajectories over 5s time horizon for longitudinal and lateral prediction RMSE of about 3.34m and 0.48m, respectively

MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.Comment: Code: https://github.com/westny/mtp-g