Trajectory Planning of Autonomous Vehicle in Freeway Driving

Ghariblu, Hashem; Moghaddam, Hossein B.

doi:10.2478/ttj-2021-0021

Abstract

This paper describes trajectory planning for an Autonomous Vehicle (AV) in the freeway path. Three types of driving modes are analyzed. First was free flow, this constitutes that moving at the desired speed is determined at the beginning of the movement. Second case was car following, when overtaking or lane-change was impossible, distance or speed adaptation is executed using the variable acceleration/deceleration strategy. Third case was lane change or overtaking. For lane change or overtaking paths, the 5th degree polynomial is used to create a curvilinear path to changes its path to the left lane and then returns to its default lane. The velocity and relative distances of cars are main factors for decision making. All proper driving decisions algorithm is introduced. According to autonomous car desired velocity, in the two driving cases (fast and slow desired velocity for AV) are studied by simulation and their results analyzed and compared with together.

References

1. Alotibi, F. Abdelhakim, M. (2020). Anomaly Detection for Cooperative Adaptive Cruise Control in Autonomous Vehicles Using Statistical Learning and Kinematic Model, IEEE Transactions on Intelligence Transportation Systems, 1-11, DOI : 10.1109/TITS.2020.2983392.
Search in Google Scholar Back to article
2. Bilik, I. Longman, O. Villeval, S. Tabrikian, J. (2019). The rise of radar for autonomous vehicles: signal processing solutions and future research directions, IEEE signal processing magazine, 36(5), 20-31.10.1109/MSP.2019.2926573
Search in Google Scholar Back to article
3. Britting, K. R. (1999). Inertial Navigation Systems Analysis, Wiley, New York.
Search in Google Scholar Back to article
4. Dias, J. E. A. Pereira, G. A. S. and Palhares, R. M. (2015) Longitudinal Model Identification and Velocity Control of an Autonomous Car, IEEE Transactions on Intelligent Transportation Systems, 16(2), 776-786, DOI: 10.1109/TITS.2014.2341491.10.1109/TITS.2014.2341491
Search in Google Scholar Back to article
5. Chen, B.-C. Bue, C.-C. (2014) Electronic Stability Control for Electric Vehicle with Four In-Wheel Motors, International Journal of Automotive Technology, 15(4), 573-580, DOI: 10.1007/s12239−014−0060−4.
Search in Google Scholar Back to article
6. Dixit S., Fallah S., Montanaro U., Dianati M., Stevens A., Mccullough F., and Mouzakitis A., (2018) Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects, Annual Rev. Control., 45, 76-86.
Search in Google Scholar Back to article
7. Ghariblu, H. (2020) Decision Making for Motion Planning of Autonomous Vehicles in the Freeway Driving, International Journal of Intelligent Transportation, Under review.
Search in Google Scholar Back to article
8. Kesting A., Treiber M., Schönhof M., Helbing D. Extending adaptive cruise control to adaptive driving strategies, Transportation Research Record, Vol. 2000 (1), pp. 16-24, 2007.
Search in Google Scholar Back to article
9. Houenou A., Bonnifait P., Cherfaoui V., and Wen Y. (2013). Vehicle Trajectory Prediction based on Motion Model and Maneuver Recognition. IEEE/RSJ International Conference on Intelligent Robots and Systems, Nov., 2013, Tokyo, Japan, 4363-4369.10.1109/IROS.2013.6696982
Search in Google Scholar Back to article
10. Kanaris A., Kosmatopoulos E. B., and Loannou P. A., (2001). Strategies and spacing requirements for lane changing and merging in automated highway system”, IEEE Trans. on Vehicular Technol., vol. 50(61), 586–1581.
Search in Google Scholar Back to article
11. Maksymova, I. Steger, C. Druml, N. (2018) Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications. Eurosensors 2018 Conferences, 2(13), 852, Austria.10.3390/proceedings2130852
Search in Google Scholar Back to article
12. Mammeri, A. Lu, G. Boukerche, A. (2015). Design of Lane Keeping Assist System for Autonomous Vehicle, 2015 7th International Conference on New Technologies, Mobility and Security (NTMS), 1-5, DOI : 10.1109/NTMS.2015.7266483.
Search in Google Scholar Back to article
13. Mampearachchi, W. K., Masakorala S. R. (2018). Analytical Model for Passing Sight Distance Design Criteria of Two-Lane Roads in Sri Lanka, Transport and Telecommunication Journal, 19(1), 10-20.10.2478/ttj-2018-0002
Search in Google Scholar Back to article
14. Milanés, V., Naranjo, J., González, C., Alonso, J., & De Pedro, T. (2008). Autonomous vehicle based in cooperative GPS and inertial systems. Robotica, 26(5), 627-633. doi:10.1017/S0263574708004232.10.1017/S0263574708004232
Search in Google Scholar Back to article
15. Nelson, W. (1989) Continuous-curvature paths for autonomous vehicles, Proceedings of the International Conference on Robotics and Automation, Scottsdale, AZ, 3, 1260-1264 DOI: 10.1109/ROBOT.1989.100153.10.1109/ROBOT.1989.100153
Search in Google Scholar Back to article
16. Patole, S. M. Torlak, M. Wang, D. and Ali, M. (2017) Automotive radars: A review of signal processing techniques. IEEE Signal Processing Magazine, 34(2), 22-35, DOI: 10.1109/MSP.2016.2628914.10.1109/MSP.2016.2628914
Search in Google Scholar Back to article
17. Sherif, M. Abuelenin, Adel Y. Abul-Magd, (2015) Effect of minimum headway distance on connectivity of VANETs, International Journal of Electronics and Communications, 69(5), 867-871, DOI: 10.1016/j.aeue.2015.01.011.10.1016/j.aeue.2015.01.011
Search in Google Scholar Back to article
18. Shim, T., Adireddy, G., and Yuan, H. (2012) Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control, P I Mech Eng D-J Aut., 226(6), 767–778.10.1177/0954407011430275
Search in Google Scholar Back to article
19. Shojaei, S. Rahmani, H. A, Azadi, S., Saeedi MA. (2020) A novel decision-making unit for automated maneuver of articulated vehicles in real traffic situations. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234(1), 152-171. DOI: 10.1177/0954407019838380.10.1177/0954407019838380
Search in Google Scholar Back to article
20. Skačkauskas, P., Sokolovskij, E. (2019) Analysis of the Hybrid Global Path Planning Algorithm for Different Environments, Transport and Telecommunication Journal, 20(1), 1-13.10.2478/ttj-2019-0001
Search in Google Scholar Back to article
21. Sukkarieh, S. Nebot, E.M. Durrant-Whyte, H.F. (1999) A high integrity IMU/GPS navigation loop for autonomous land vehicle applications, IEEE Transactions on Robotics and Automation, 15(3), 572-578, DOI : 10.1109/70.768189.
Search in Google Scholar Back to article
22. Tarel, J. Hautiere, N. Caraffa, L. Cord, A. Halmaoui, H. and Gruyer, D. (2012) Vision Enhancement in Homogeneous and Heterogeneous Fog, IEEE Intelligent Transportation Systems Magazine, 4(2), 6-20, DOI: 10.1109/MITS.2012.2189969.10.1109/MITS.2012.2189969
Search in Google Scholar Back to article
23. Wang, L. Zhang, Y.Wang, J. (2017) Map-based localization method for autonomous vehicles using 3D-lidar, IFAC PapersOnLine, 50(1), 276-281.10.1016/j.ifacol.2017.08.046
Search in Google Scholar Back to article
24. Yang, Q., Koutsopoulos, H. N. (1996) A microscopic traffic simulator for evaluation of dynamic traffic management systems, Transportation Research, Part C: Emerging Technologies, 4(3), 113-129.10.1016/S0968-090X(96)00006-X
Search in Google Scholar Back to article
25. Zaarane, A. Slimani, I. Al Okaishi, W. Atouf, I. Hamdoun, A. (2020) Distance measurement system for autonomous vehicles using stereo camera, Array. 5:100016, DOI: 10.1016/j.array.2020.100016.10.1016/j.array.2020.100016
Search in Google Scholar Back to article
26. Zhang, S., Deng, W., Zhao, Q., Sun, H., Litkouhi, B. (2013) Dynamic trajectory planning for vehicle autonomous driving. 2013 IEEE International Conference on Systems, Man, and Cybernetics, Oct., 2013, Manchester, UK, 4161–4166.10.1109/SMC.2013.709
Search in Google Scholar Back to article

Trajectory Planning of Autonomous Vehicle in Freeway Driving

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