Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

2019 IEEE Intelligent Vehicles Symposium (IV)

et al. 2019

For an efficient integration of autonomous vehicles on roads, human-like reasoning and decision making in complex traffic situations are needed. One of the key factors to achieve this goal is the estimation of the future behavior of the vehicles present in the scene. In this work, we propose a new approach to predict the motion of vehicles surrounding a target vehicle in a highway environment. Our approach is based on an LSTM encoder-decoder that uses a social pooling mechanism to model the interactions between all the neighboring vehicles. The originality of our social pooling module is that it combines both local and non-local operations. The non-local multi-head attention mechanism captures the relative importance of each vehicle despite the inter-vehicle distances to the target vehicle, while the local blocks represent nearby interactions between vehicles. This paper compares the proposed approach with the state-of-the-art using two naturalistic driving datasets: Next Generation Simulation (NGSIM) and the new highD Dataset. The proposed method outperforms existing ones in terms of RMS values of prediction error, which shows the effectiveness of combining local and non-local operations in such a context.

Section: Deep-learning Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-local Social Pooling for Vehicle Trajectory Prediction

2019 IEEE Intelligent Vehicles Symposium (IV)

et al. 2019

“…3) Prediction: Vehicle intent prediction is divided into two main aspects: maneuver [4], [23] and trajectory prediction [5], [24], [8]. The former generates a high-level representation of the motion such as lane changing and lane keeping.…”

Section: A Overall Motion Prediction Modulementioning

confidence: 99%

Attention Based Vehicle Trajectory Prediction

IEEE Trans. Intell. Veh.

et al. 2021

199

Self-driving vehicles need to continuously analyse the driving scene, understand the behavior of other road users and predict their future trajectories in order to plan a safe motion and reduce their reaction time. Motivated by this idea, this paper addresses the problem of vehicle trajectory prediction over an extended horizon. On highways, human drivers continuously adapt their speed and paths according to the behavior of their neighboring vehicles. Therefore, vehicles' trajectories are very correlated and considering vehicle interactions makes motion prediction possible even before the start of a clear maneuver pattern. To this end, we introduce and analyze trajectory prediction methods based on how they model the vehicles interactions. Inspired by human reasoning, we use an attention mechanism that explicitly highlights the importance of neighboring vehicles with respect to their future states. We go beyond pairwise vehicle interactions and model higher order interactions. Moreover, the existence of different goals and driving behaviors induces multiple potential futures. We exploit a combination of global and partial attention paid to surrounding vehicles to generate different possible trajectory. Experiments on highway datasets show that the proposed model outperforms the state-of-the-art performances.

“…Different LSTM-based approaches have been used; A simple LSTM with one or more layers was utilized in [5], [6], [7], [10]. Xin et al [8] use a dual LSTM. The first one for high-level driver intention recognition succeeded by a second generating the corresponding predicted trajectory.…”

Section: A Independent Predictionmentioning

confidence: 99%

“…Predicting the trajectory of a vehicle is a challenging task since it is highly correlated to other drivers' behaviors. Many studies tackle this task using traditional data-driven techniques [1], [2], [3] as well as deep learning models [4], [5], [6], [7], [8], [9], [10]. LSTMs have shown great success in modeling temporal data.…”

Section: Introductionmentioning

confidence: 99%

Relational Recurrent Neural Networks For Vehicle Trajectory Prediction

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

et al. 2019

Scene 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.