Abstract
We propose a steering control algorithm for autonomous backward driving in a narrow corridor. Passable spaces are detected using a stereo camera, and the steering angle is controlled by a model predictive controller (MPC). For passable space detection, an UV-disparity map is calculated from the original disparity map. Information regarding passable spaces collected by the stereo camera is used in steering control. Backward driving requires the driver’s preemptive actions, which can be learned by experience because of the non-intuitive responses (the initial motion of the vehicle is opposite to the driver’s steering angle input). This occurs because a backward-driving vehicle is a non-minimum phase system. One of the most popular steering control algorithms is Stanley method, which is based on the feedback of lateral displacement error and heading angle error. The method is very intuitive and works well for forward driving, but it exhibits significant undershoot for backward driving cases. Furthermore, the method does not explicitly consider any constraints on control inputs and states. We designed a steering controller based on the MPC technique that requires future information but can handle constraints explicitly. Because we have near-future information from the stereo camera under limited passable spaces, MPC can be effectively implemented. We performed several simulations and experiments to show the performance and superiority of the suggested method over a simple feedback-based control algorithm.
Similar content being viewed by others
References
Ballard, D. H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13, 2, 111–122.
Bertozzi, M. and Broggi, A. (1998). GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Processing 7, 1, 62–81.
Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, Inc. Sebastopol, California, USA.
Canny, J. (1986). A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence PAMI-8, 6, 679–698.
Choi, H.-C., Park, J.-M., Choi, W.-S. and Oh, S.-Y. (2012). Vision-based fusion of robust lane tracking and forward vehicle detection in a real driving environment. Int. J. Automotive Technology 13, 4, 653–669.
Fernández, C., Llorca, D. F., Sotelo, M. A., Daza, I. G., Hellín, A. M. and Álvarez, S. (2013). Real-time visionbased blind spot warning system: Experiments with motorcycles in daytime/nighttime conditions. Int. J. Automotive Technology 14, 1, 113–122.
Han, J., Kim, D., Lee, M. and Sunwoo, M. (2014). Road boundary detection and tracking for structured and unstructured roads using a 2D lidar sensor. Int. J. Automotive Technology 15, 4, 611–623.
Hu, Z. and Uchimura, K. (2005). UV-disparity: An efficient algorithm for stereovision based scene analysis. IEEE Intelligent Vehicles Symp., Las Vegas, USA, 48–54.
Jadbabaie, A., Lin, J. and Stephen Morse, A. (2003). Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Automatic Control 48, 6, 988–1001.
Jeong, J.-B., Oh, J. S. and Kim, J. H. (2013). Research of terrain classification using disparity value for UGV. Int. Symp., Robotics (ISR), Seoul, Korea, 1–5.
Jeong, S. H., Choi, C. G., Oh, J. N., Yoon, P. J., Kim, B. S., Kim, M. and Lee, K. H. (2010). Low cost design of parallel parking assist system based on an ultrasonic sensor. Int. J. Automotive Technology 11, 3, 409–416.
Jeong, S. H., Lee, J. E., Choi, S. U., Oh, J. N. and Lee, K. H. (2012). Technology analysis and low-cost design of automotive radar for adaptive cruise control system. Int. J. Automotive Technology 13, 7, 1133–1140.
Koenderink, J. J. and Van Doorn, A. J. (1991). Affine structure from motion. J. Optical Society of America A 8, 2, 377–385.
Kim, M., Shin, S. and Park, J. (2016). Study on vehicle lateral control for backward driving. Int. Conf. Ubiquitous Robots and Ambient Intelligence, Xian, China, 191–193.
Mallot, H. A., Bülthoff, H. H., Little, J. J. and Bohrer, S. (1991). Inverse perspective mapping simplifies optical flow computation and obstacle detection. Biological Cybernetics 64, 3, 177–185.
Mayne, D. Q., Rawlings, J. B., Rao, C. V. and Scokaert, P. O. M. (2000). Constrained model predictive control: Stability and optimality. Automatica 36, 6, 789–814.
Milanés, V., González, C., Naranjo, J. E., Onieva, E. and De Pedro, T. (2010). Electro-hydraulic braking system for autonomous vehicles. Int. J. Automotive Technology 11, 1, 89–95.
Suzuki, S. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing 30, 1, 32–46.
Texas-Instruments (2015). Advanced Driver Assistance (ADAS) Solutions Guide.
Pamardhan, S., Tan, H. S. and Guldner, J. (1997). Lane following during backward driving for front wheel steered vehicles. American Control Conf., Albuquerque, New Mexico, 3348–3353.
Rajamani, R., Zhu, C. and Alexander, L. (2003). Lateral control of a backward driven front-steering vehicle. Control Engineering Practice 11, 5, 531–540.
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Hapenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V. and Stang, P. (2006). Stanley: The robot that won the DARPA Grand Challenge. J. Field Robotics 23, 9, 661–692.
Yoshida, M. and Yamashita, Y. (2013). Simultaneous estimation of vehicle sideslip angle and tire-road friction coefficient. SICE Annual Conf., Nagoya, Japan, 1586–1591.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Son, CW., Choi, W. & Ahn, C. MPC-BASED steering control for backward-driving vehicle using stereo vision. Int.J Automot. Technol. 18, 933–942 (2017). https://doi.org/10.1007/s12239-017-0091-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-017-0091-8