Lane departure prediction based on closed-loop vehicle dynamics

Abstract

An automated driving system should have the ability to supervise its own performance and to request human driver to take over when necessary. In the lane keeping scenario, the prediction of vehicle future trajectory is the key to realize safe and trustworthy driving automation. Previous studies on vehicle trajectory prediction mainly fall into two categories, that is, physics-based and manoeuvre-based methods. Using a physics-based methodology, this article proposes a lane departure prediction algorithm based on closed-loop vehicle dynamics model. We use extended Kalman filter to estimate the current vehicle states based on sensing module outputs. Then, a Kalman Predictor with actual lane keeping control law is used to predict steering actions and vehicle states in the future. A lane departure assessment module evaluates the probabilistic distribution of vehicle corner positions and decides whether to initiate a human takeover request. The prediction algorithm is capable to describe the stochastic characteristics of future vehicle pose, which is preliminarily proved in simulated tests. Finally, the on-road tests at speeds of 15–50 km/h further show that the proposed method can accurately predict vehicle future trajectory. It may work as a promising solution to lane departure risk assessment for automated lane keeping functions.

Keywords

Trajectory prediction Kalman predictor lane keeping assist automated driving risk assessment

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References

Ammoun

Nashashibi

. Real time trajectory prediction for collision risk estimation between vehicles. In: Proceedings of the 2009 IEEE 5th international conference on intelligent computer communication and processing, Cluj-Napoca, 27–29 August 2009, pp.417–422. New York: IEEE.

Polychronopoulos

Tsogas

Amditis

, et al. Sensor fusion for predicting vehicles’ path for collision avoidance systems. IEEE T Intell Transp 2007; 8(3): 549–562.

Batz

Watson

Beyerer

. Recognition of dangerous situations within a cooperative group of vehicles. In: Proceedings of the 2009 IEEE intelligent vehicles symposium, Xi’an, China, 3–5 June 2009, pp.907–912. New York: IEEE.

Schubert

Richter

Wanielik

. Comparison and evaluation of advanced motion models for vehicle tracking. In: Proceedings of the 2008 11th international conference on information fusion, Cologne, 30 June–3 July 2008, pp.1–6. New York: IEEE.

Xie

Gao

Qian

, et al. Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models. IEEE T Ind Electron 2018; 65(7): 5999–6008.

Xiao

Zhang

Meng

. Vehicle trajectory prediction based on motion model and maneuver model fusion with interactive multiple models. In: Proceedings of the WCX 2020 SAE world congress experience, Detroit, MI, 21–24 April 2020. Warrendale, PA: SAE International.

Lefèvre

Vasquez

Laugier

. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J 2014; 1(1): 1–14.

Tan

H-S

Huang

. DGPS-based vehicle-to-vehicle cooperative collision warning: engineering feasibility viewpoints. IEEE T Intell Transp 2006; 7(4): 415–428.

Brannstrom

Coelingh

Sjoberg

. Model-based threat assessment for avoiding arbitrary vehicle collisions. IEEE T Intell Transp 2010; 11(3): 658–669.

10.

Eidehall

Petersson

. Statistical threat assessment for general road scenes using Monte Carlo sampling. IEEE T Intell Transp 2008; 9(1): 137–147.

11.

Wang

Zhao

Han

, et al. A learning-based approach for lane departure warning systems with a personalized driver model. IEEE T Veh Technol 2018; 67(10): 9145–9157.

12.

Yuan

Wang

. Lane-change prediction method for adaptive cruise control system with hidden Markov model. Adv Mech Eng 10(9): 1–9.

13.

Gindele

Brechtel

Dillmann

. A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. In: Proceedings of the 13th international IEEE conference on intelligent transportation systems, Funchal, 19–22 September 2010, pp.1625–1631. New York: IEEE.

14.

Kumar

Perrollaz

Lefèvre

, et al. Learning-based approach for online lane change intention prediction. In: Proceedings of the 2013 IEEE intelligent vehicles symposium (IV), Gold Coast, QLD, Australia, 23–26 June 2013, pp.797–802. New York: IEEE.

15.

Izquierdo

Parra

Muñoz-Bulnes

, et al. Vehicle trajectory and lane change prediction using ANN and SVM classifiers. In: Proceedings of the 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), Yokohama, Japan, 16–19 October 2017, pp.1–6. New York: IEEE.

16.

Kim

Kang

Kim

, et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In: Proceedings of the 2017 IEEE 20th international conference on intelligent transportation systems, Yokohama, Japan, 16–19 October 2017, pp.399–404. New York: IEEE.

17.

Deo

Trivedi

. Convolutional social pooling for vehicle trajectory prediction. In: Proceedings of the 2018 IEEE/CVF conference on computer vision and pattern recognition workshops, Salt Lake City, UT, 18–22 June 2018, pp.1468–1476. New York: IEEE.

18.

Djuric

Radosavljevic

Cui

, et al. Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving. In: Proceedings of the 2020 IEEE winter conference on applications of computer vision, Snowmass, CO, 2–5 March 2020, pp.2084–2093. New York: IEEE.

19.

Deo

Trivedi

. Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. In: Proceedings of the 2018 IEEE intelligent vehicles symposium (IV), Changshu, China, 26–30 June 2018, pp.1179–1184. New York: IEEE.

20.

Tan

Chen

Wang

. On the use of Monte-Carlo simulation and deep Fourier neural network in lane departure warning. IEEE Intel Transp Sy Mag 2017; 9(4): 76–90.

21.

Boulton

Grigore

Wolff

. Motion prediction using trajectory sets and self-driving domain knowledge, 8 June 2020, https://arxiv.org/abs/2006.04767

22.

Zhan

Tomizuka

. Probabilistic prediction of vehicle semantic intention and motion. In: Proceedings of the 2018 IEEE intelligent vehicles symposium (IV), Changshu, China, 26–30 June 2018, pp.307–313. New York: IEEE.

23.

. Theory of automobile. 4th ed. Beijing, China: Machinery Industry Press, 2009.

24.

Rajamani

. Vehicle dynamics and control. 2nd ed. Boston, MA: Springer, 2012.

25.

Deng

. Robust fusion Kalman filter theory and its application. Harbin, China: Harbin Institute of Technology Press, 2015.

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