Research on vehicle trajectory prediction algorithm based on improved transformer

Abstract

Trajectory prediction is an important part in autonomous driving technology. The current prediction algorithm model still has some problems in the interaction behavior of traffic participants, the utilization rate of highly refined map information, and the learning of potential traffic rules in the real world. In this paper, a vehicle trajectory prediction algorithm model based on neural network is built, which selects Argoverse 2 Motion Forecasting as the training data set of the model. Relative spatiotemporal position encoding is used to encode the input information of the trajectory prediction model. It can better understand the complex interaction behavior in traffic scenes. The K-Means++ algorithm is used for the trajectory clustering of the Waymo dataset, integrating their results as prior knowledge into the trajectory prediction model to enhance their ability of learning from the potential traffic rules in the real world. The performance of the model was tested according to the selected data set. The average displacement error, the final displacement error, and its minimum value are used as the evaluating indicator of output trajectory and perform quantitative analysis. Visualizing the prediction trajectory of the model to visually judge the error and feasibility of the model. The results prove that the prediction model has good prediction ability for some behaviors such as going straight, turning left, and turning right. It can also well predict the future state information of other traffic participants such as pedestrians and non-motor vehicles.

Keywords

Trajectory prediction neural networks positional encoding attention mechanism prior knowledge

Get full access to this article

View all access options for this article.

References

Irio

Oliveira

. Vehicle trajectory prediction based on LSTM recurrent neural networks. In: IEEE 93rd vehicular technology conference (VTC-Spring), Helsinki, Finland, 25–28 April 2021.

Brannstrom

Coelingh

Sjoberg

. Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions. IEEE Trans Intell Transp Syst 2010; 11: 658–669.

Lytrivis

Thomaidis

Amditis

Cooperative path prediction in vehicular environments. In: 11th IEEE international conference on intelligent transportation systems (ITSC 2008), Beijing, China, 12–15 October 2008, pp.803–808. New York: IEEE.

Barth

Franke

Where will the oncoming vehicle be the next second? In: IEEE intelligent vehicles symposium, Eindhoven, Netherlands, 4–6 June 2008, pp.510–515. New York: IEEE.

Ammoun

Nashashibi

. Real time trajectory prediction for collision risk estimation between vehicles. In: 5th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2009), Cluj-Napoca, Romania, 27–29 August 2009, pp. 347–352. New York: IEEE.

Leu

Wang

Tomizuka

, et al. Autonomous vehicle parking in dynamic environments: An integrated system with prediction and motion planning. arXiv preprint arXiv:2204.10383. 2022.

Tran

Firl

. Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression. In: IEEE intelligent vehicles symposium (IV), Dearborn, MI, USA, 8–11 June 2014, pp.924–929. New York: IEEE.

Wang

Liu

Zuo

, et al. Trajectory planning and safety assessment of autonomous vehicles based on motion prediction and model predictive control. IEEE Trans Veh Technol 2019; 68: 8546–8556.

Heravi

Khanmohammadi

. Long term trajectory prediction of moving objects using Gaussian process. In: International conference on robot, vision and signal processing, Kaohsiung, Taiwan, 21–23 November 2011.

10.

Goli

Far

Fapojuwo

. Vehicle trajectory prediction with Gaussian process regression in connected vehicle environment. In: IEEE Intelligent vehicles symposium (IV), Changshu, China, 26–30 June 2018, pp.550–555. New York: 2018.

11.

Zhang

Wang

, et al. Vehicle trajectory prediction based on Hidden Markov Model. KSII Trans Internet Inf Syst 2016; 10: 3150–3170.

12.

Qiao

Shen

Wang

, et al. A self-adaptive parameter selection trajectory prediction approach via Hidden Markov Models. IEEE Trans Intell Transp Syst 2015; 16: 284–296.

13.

Qiao

Wang

, et al. Short-term traffic flow prediction based on 1DCNN-LSTM neural network structure. Mod Phys Lett B 2021; 35: 17.

14.

Wang

Qiao

, et al. A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction. Physica A 2021; 583: 13.

15.

Dai

. Modeling Vehicle Interactions via Modified LSTM Models for Trajectory Prediction. IEEE Access 2019; 7: 38287–38296..

16.

Khakzar

Rakotonirainy

Bond

, et al. A dual learning model for vehicle trajectory prediction. IEEE Access 2020; 8: 21897–21908.

17.

Cui

Zhou

Zhu

, et al. A road-aware neural network for multi-step vehicle trajectory prediction. In: 23rd international conference on database systems for advanced applications (DASFAA), Gold Coast, QLD, Australia, 2018, vol. 10827, pp.701–716. Cham: Springer International Publishing.

18.

Xiao

Wang

, et al. UB-LSTM: A Trajectory Prediction Method Combined with Vehicle Behavior Recognition. J Adv Transp 2020; 2020: 1–12.

19.

Eiffert

Shan

, et al. Probabilistic crowd GAN: multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian attention network. IEEE Robot Autom Lett 2020; 5: 5026–5033.

20.

Choi

Ahn

, et al. Future trajectory prediction via RNN and maximum margin inverse reinforcement learning. In: 17th IEEE international conference on machine learning and applications (IEEE ICMLA), Orlando, FL, USA, 17–20 December 2018, pp.125–130. New York: IEEE.