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Intention-aware Lane Changing Assistance Strategy Basing on Traffic Situation Assessment
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation. Traffic situation is assessed according to the index of the motion intentions and the coupling tendency between the ego vehicle and the obstacle vehicles and the risk level is graded basing on the map with collision time. Lane keeping assist is carried out according to the assessment result of the traffic situation. Testing scenarios with the straight road and T-junction are designed and a co-simulation environment consisting of CarMaker and Mathwork Simulink is established to verify the proposed strategy in complex traffic scenes. Simulation results present an adaptive ROI and a high identification accuracy for motion intentions of the obstacle vehicles. What’s more, it shows that the traffic situation can be accurately evaluated and the ego vehicle can be effectively controlled with the appearance of the high-risk vehicles.
CitationWu, J., Liu, S., He, R., and Sun, B., "Intention-aware Lane Changing Assistance Strategy Basing on Traffic Situation Assessment," SAE Technical Paper 2020-01-0127, 2020, https://doi.org/10.4271/2020-01-0127.
Data Sets - Support Documents
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- Chang, S.-M., Tsai, C.-C., and Guo, J.-I., “A Blind Spot Detection Warning System Based on Gabor Filtering and Optical Flow for E-Mirror Applications,” in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, 2018, 1-5, doi:10.1109/ISCAS.2018.8350927, IEEE.
- Ra, M., Jung, H.G., Suhr, J.K., and Kim, W.-Y., “Part-Based Vehicle Detection in Side-Rectilinear Images for Blind-Spot Detection,” Expert Systems with Applications 101:116-128, 2018, doi:10.1016/j.eswa.2018.02.005.
- Zhao, Y., Bai, L., Lyu, Y., and Huang, X., “Camera-Based Blind Spot Detection with a General Purpose Lightweight Neural Network,” Electronics 8(2):233, 2019, doi:10.3390/electronics8020233.
- Liu, G., Zhou, M., Wang, L., Wang, H., and Guo, X., “A Blind Spot Detection and Warning System Based on Millimeter Wave Radar for Driver Assistance,” Optik 135:353-365, 2017, doi:10.1016/j.ijleo.2017.01.058.
- Hyun, E., Jin, Y.S., and Lee, J.H., “Design and Development of Automotive Blind Spot Detection Radar System Based on ROI Pre-Processing Scheme,” Int. J Automot. Technol. 18(1):165-177, 2017, doi:10.1007/s12239-017-0017-5.
- Wang, Y., Liu, Z., and Deng, W., “Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection,” Sensors 19(5):1089, 2019, doi:10.3390/s19051089.
- Li, M., Song, X., Cao, H., and Huang, Z., “Shared Steering Control Combined with Driving Intention for Vehicle Obstacle Avoidance,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233(11):2791-2808, 2019, doi:10.1177/0954407018806147.
- Xiong, L., Teng, G.W., Yu, Z.P., Zhang, W.X., and Feng, Y., “Novel Stability Control Strategy for Distributed Drive Electric Vehicle Based on Driver Operation Intention,” Int. J Automot. Technol. 17(4):651-663, 2016, doi:10.1007/s12239-016-0064-3.
- Li, K., Wang, X., Xu, Y., and Wang, J., “Lane Changing Intention Recognition Based on Speech Recognition Models,” Transportation Research Part C: Emerging Technologies 69:497-514, 2016, doi:10.1016/j.trc.2015.11.007.
- Wang, S., Yu, Q., and Zhao, X., “Study on driver’s Turning Intention Recognition Hybrid Model of GHMM and GGAP-RBF Neural Network,” Advances in Mechanical Engineering 10(3):168781401876498, 2018, doi:10.1177/1687814018764985.
- Houenou, A., Bonnifait, P., Cherfaoui, V., and Wen, Y., “Vehicle Trajectory Prediction Based on Motion Model and Maneuver Recognition,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Tokyo, 4363-4369, 2013, doi:10.1109/IROS.2013.6696982.
- Zhang, R., Cao, L., Bao, S., and Tan, J., “A Method for Connected Vehicle Trajectory Prediction and Collision Warning Algorithm Based on V2V Communication,” International Journal of Crashworthiness 22(1):15-25, 2017, doi:10.1080/13588265.2016.1215584.
- Augustin, D., Hofmann, M., and Konigorski, U., “Prediction of Highway Lane Changes Based on Prototype Trajectories,” Forsch Ingenieurwes 83(2):149-161, 2019, doi:10.1007/s10010-019-00321-0.
- Wang, J., Yu, C., Li, S.E., and Wang, L., “A Forward Collision Warning Algorithm with Adaptation to Driver Behaviors,” IEEE Trans. Intell. Transport. Syst. 17(4):1157-1167, 2016, doi:10.1109/TITS.2015.2499838.
- Rajaram, V. and Subramanian, S.C., “Heavy Vehicle Collision Avoidance Control in Heterogeneous Traffic Using Varying Time Headway,” Mechatronics 50:328-340, 2018, doi:10.1016/j.mechatronics.2017.11.010.
- Reynolds, D., “Gaussian Mixture Models,” in: Li, S.Z. and Jain, A.K., eds., Encyclopedia of Biometrics (Boston, MA: Springer US, 2015), 827-832, doi:10.1007/978-1-4899-7488-4_196.