Long-Term Prediction of Vehicle Trajectory Using Recurrent Neural Networks

The expectations regarding autonomous vehicles are very high to transform the future mobility and ensure more road safety. Autonomous driving system should be able in the short term to detect dangerous situations and respond appropriately and thus increase driving safety. Understanding the intentions of drivers has recently received growing interest. A long-term prediction method based on gated unit-recurrent neural network model is proposed for the problem of trajectory prediction of surrounding vehicles. A deep neural network with Long-short term memory (LSTM) and Gated Recurrent Units (GRU) structure is used to analyze the spatial-temporal features of the past trajectory. Through sequences learning, the system generates the future trajectory of other traffic participants for different horizons of prediction. We evaluate all models with standard metric (Root mean square error RMSE), loss function convergence and processing time. After comparing the different models, our experiments revealed that the proposed GRU based models is indeed better than LSTM based models in term of accuracy and processing speed.

[1]  K.C.J. Dietmayer,et al.  IMM object tracking for high dynamic driving maneuvers , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[2]  Nanning Zheng,et al.  Human-Like Maneuver Decision Using LSTM-CRF Model for On-Road Self-Driving , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[3]  Florent Altché,et al.  An LSTM network for highway trajectory prediction , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[4]  Mathias Perrollaz,et al.  Learning-based approach for online lane change intention prediction , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[5]  Christian Laugier,et al.  Probabilistic Vehicle Motion Modeling and Risk Estimation , 2012 .

[6]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014, ROBOMECH Journal.

[7]  Dongsuk Kum,et al.  The multilayer perceptron approach to lateral motion prediction of surrounding vehicles for autonomous vehicles , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[8]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[9]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[10]  Chung Choo Chung,et al.  Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[11]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[12]  Mykel J. Kochenderfer,et al.  Generalizable intention prediction of human drivers at intersections , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[13]  Jianyu Chen,et al.  Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[14]  Chung Choo Chung,et al.  Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[15]  Karl-Heinz Hoffmann,et al.  Prediction of driver intended path at intersections , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[16]  John Tran,et al.  cuDNN: Efficient Primitives for Deep Learning , 2014, ArXiv.

[17]  Hiren M. Mandalia,et al.  Using Support Vector Machines for Lane-Change Detection , 2005 .

[18]  Mohan M. Trivedi,et al.  Surround vehicles trajectory analysis with recurrent neural networks , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[19]  Fawzi Nashashibi,et al.  Real time trajectory prediction for collision risk estimation between vehicles , 2009, 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing.

[20]  Mohan M. Trivedi,et al.  Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[21]  Alexiadis,et al.  The next generation simulation program , 2004 .