Abstract
During a lane change, vehicle collision warning systems detect the likelihood of collision and time to collision to warn vehicles of an imminent collision. In autonomous systems, a vehicle utilizes data obtained by its own sensors to predict future state of traffic. The data from on board sensors are limited by line of sight, measurement noise and motion parameters of the vehicle which affect the accuracy of prediction. Alternatively, in cooperative driving, vehicles transmit their parameters continuously. This is also beset by communication delays and message loss. To avoid these limitations, a vehicle should estimate its future state and broadcast it to others vehicles in the neighborhood. This necessitates vehicles to predict their future trajectories based entirely on its past. Since, low cost global positioning systems are becoming an integral part of vehicles; a vehicle knows its own position. This can be utilized by the vehicle for prediction of its future trajectory. In this paper, the effectiveness of lane change trajectory prediction on the basis of past positions is studied. The lane change trajectory of a vehicle is modeled as a time series and back propagation neural network is trained using real field data and its efficacy for short range and long range prediction is benchmarked. Simulation results using NGSIM data indicate that future lane change trajectory cannot be predicted with sufficient accuracy. The most important reason seemed to be the influence of other neighboring vehicles on the trajectory on the lane changing vehicle in addition to noise and complex dependence of future on the past values. The results also indicate that a vehicle changes its motion parameters during the entire lane change process. This confirms the active intervention of the driver in adjusting the trajectory on the basis of his assessment of the future state of its surrounding vehicles and entails the consideration of the state of surrounding vehicle for accurate prediction.
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References
Ohno, H.: Analysis and Modeling of Human Driving Behaviors Using Adaptive Cruise Control. In: IEEE 26th Annual conference of Industrial Electronics (IECON 2000), pp. 2803–2808 (2000)
Naranjo, J.E., Gonzalez, C., Garcia, R., Pedro, T.D.: Cooperative Throttle and Brake Fuzzy Control for ACC+ Stop & Go Maneuvers. IEEE Transactions on Vehicular Technology 56(4), 1623–1629 (2007)
Roessler, B., Fuerstenberg, K.: Sensor Based Intersection Reconstruction for a Cooperative Intersection Safety System. In: IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2009), pp. 393–399 (2009)
Chan, C.Y., Bougler, B.: Evaluation of Cooperative Roadside and Vehicle- Based Data collection for Assessing Intersection Conflicts. In: IEEE International Intelligent Vehicle Symposium, pp. 165–170 (2005)
Kaneko, J.F., Imamura, A.: A Design of Safety Support Maneuvers for Automated Vehicles Cruising Around Intersections. In: IEEE International American Control Conference, vol. 5, pp. 3518–3522 (2000)
Huang, J., Tan, H.S.: Design and Implementation of a Cooperative Collision Warning System. In: IEEE International Intelligent Transportation Systems Conference (ITSC 2006), pp. 1017–1022 (2006)
Girado, A.R., De Sousa, J.B., Misener, J.A., Hedrick, J.K.: A Control Architecture for Integrated Cooperative Cruise Control and Collision Warning Systems. In: IEEE 40th International Conference on Decision and Control, vol. 2, pp. 1491–1496 (2001)
Zhu, B., Xia, W., Song, T., Shen, L.: Position Based Broadcast Mechanism for Inter-Vehicle Cooperative Warning. In: IEEE 1st International Conference on Information Science and Engineering (ICISE 2009), pp. 2683–2686 (2009)
Tu, L., Huang, C.M.: Forwards: A Map Free Intersection Collision Warning System for All Road Patterns. In: IEEE Transactions on Vehicular Technology (2010) (to appear)
Hossein, J., Kosmatopoulos, E.B., Ioannou, P.A.: Collision Avoidance Analysis for Lane Changing and Merging. IEEE Transaction on Vehicular Technology 49(6), 2295–2308 (2000)
Errampalli, M., Okushima, M., Akiyama, T.: Fuzzy Logic Based Lane Change Model for Microscopic Traffic Flow Simulation, In. Journal of Advanced Computational Intelligence and Intelligent Informatics 12(2), 172–173 (2008)
Ghumman, U., Kunwar, F., Benhabib, B.: Guidance-Based on-line motion planning for autonomous highway overtaking. International Journal on Smart Sensing and Intelligent System 1(2), 549–571 (2008)
Kanaris, A., Kosmatopoulos, E.B., Loannou, P.A.: Strategies and Spacing Requirements for Lane Changing and Merging in Automated Highway Systems. IEEE Transaction on Vehicular Technology 50(6), 1568–1581 (2001)
Chaurasia, B.K., Verma, S.: Haste Induced Behavior and VANET Communication. In: IEEE International Conference on Vehicular Electronics and Safety (ICVES 2009), pp. 19–24 (2009)
Caveney, D.: Cooperative vehicular safety application. IEEE Control System Magazine, 38–53 (2010)
Thiemann, M.T., Kesting, A.: Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data. Transportation Research Record 2088, 90–101 (2008)
Kesting, A., Treiber, M., Helbing, A.: General Lane-Changing Model MOBIL for Car-Following Models. Journal of the Transportation Research Board, No. 1999, Transportation Research Board of the National Academies, 86–94 (2007)
Sun, Y., Loannon, P.: A hand book for Inter-vehicle Spacing in Vehicle Following (1995)
Malta, L., Miyajima, C., Takeda, K.: A Study of Driver Behavior Under Potential Threats in Vehicle Traffic. IEEE Transactions on Intelligent Transportation Systems 10(2), 201–210 (2009)
Nishiwaki, Y., Miyajima, C., Kitaoka, H., Takeda, K.: Stochastic Modeling of Vehicle Trajectory during Lane-Changing. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), pp. 1377–1380 (2009)
Toledo-Moreo, R., Zamora-Izquierdo, M.A., Gómez-Skarmeta, A.F.: Multiple Model based Lane Change Prediction for Road Vehicles with Low Cost GPS/IMU. In: IEEE International Conference on Intelligent Transportation Systems (ITSC 2007), pp. 473–478 (2007)
Lu, X.-Y., Skabardonis, A.: Freeway Traffic Shockwave Analysis: Exploring the NGSIM Trajectory Data. In: 86th Annual Meeting Transportation Research Board, Washington, D.C, pp. 1–19 (2007)
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Tomar, R.S., Verma, S. (2011). Neural Network Based Lane Change Trajectory Prediction in Autonomous Vehicles. In: Gavrilova, M.L., Tan, C.J.K. (eds) Transactions on Computational Science XIII. Lecture Notes in Computer Science, vol 6750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22619-9_7
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DOI: https://doi.org/10.1007/978-3-642-22619-9_7
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