This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Forward Collision Warning System Using Deep Reinforcement Learning
Technical Paper
2020-01-0138
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Forward collision warning is one of the most challenging concerns in the safety of autonomous vehicles. A cooperation between many sensors such as LIDAR, Radar and camera helps to enhance the safety. Apart from the importance of having a reliable object detector, the safety system should have requisite capabilities to make reasonable decisions in the moment. In this work, we concentrate on detecting front vehicles of autonomous cars using a monocular camera, beyond only a detection method. In fact, we devise a solution based on a cooperation between a deep object detector and a reinforcement learning method to provide forward collision warning signals. The proposed method models the relation between acceleration, distance and collision point using the area of the bounding box related to the front vehicle. An agent of learning automata as a reinforcement learning method interacts with the environment to learn how to behave in eclectic hazardous situations. The agent follows a deterministic but variable structure learning automata in order to find the collision point in different status. The proposed learning automata method has a nested structure in its states and every state has a memory to place the time duration between the entrance and the collision point. The Agent makes decisions based on a specific number of previous data to engross the impact of time. Since it is almost impossible to capture the data of hazardous situations, we design the essential scenarios via a virtual simulation environment. Eventually, after some trails and errors the algorithm reaches to a convergence point and extracts the model of front vehicle motions.
Authors
Citation
Fekri, P., Abedi, V., Dargahi, J., and Zadeh, M., "A Forward Collision Warning System Using Deep Reinforcement Learning," SAE Technical Paper 2020-01-0138, 2020, https://doi.org/10.4271/2020-01-0138.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
[Unnamed Dataset 1] | ||
[Unnamed Dataset 2] | ||
[Unnamed Dataset 3] |
Also In
References
- World Health Organization , Global Status Report on Road Safety 2015, World Health Organization, 2015, 323.
- Najm, W.G., Stearns, M.D. Howarth, H., Koopman, J., and Hitz, J. , “Evaluation of an Automotive Rear-End Collision Avoidance System,” DOT VNTSC-NHTSA-06-01, DOT HS 810 569, 2006.
- Abe, G. and Richardson, J. , “The Effect of Alarm Timing on Driver Behaviour: An Investigation of Differences in Driver Trust and Response to Alarms According to Alarm Timing,” Transp. Res. F, Traffic Psychol. Behav. 7:307-322, 2004.
- Zhang, Y., Yan, X., Li, X., and Xue, Q. , “Effects of Collision Warning System Under Different Warning Timing on Driving Speed and Distance,” in International Conference on Transportation Information and Safety, Wuhan, 2015.
- Brunson, S., Kyle, E., Phamdo, N., and Preziotti, G. , “Alert Algorithm Development Program: NHTSA Rear-End Collision Alert Algorithm,” Nat. Highway Traffic Safety Admin., U.S. Dept. Transp., Washington, USA, 2002.
- Dagan, E., Mano, O., Stein, G.P., and Shashua, A. , “Forward Collision Warning with a Single Camera,” Proc. IEEE Intell. Vehicles Symp., 2004.
- Chin, C., Quek, S.T., and Cheu, R.L. , “Traffic Conflicts in Expressway Merging,” J. Transp. Eng., 1991.
- Kenue, S.K. , “Selection of Range and Azimuth Angle Parameters for a Forward Looking Collision Warning Radar Sensor,” in Proceedings of the Intelligent Vehicles ’95 Symposium, 1995.
- Srinivasa, N., Chen, Y., and Daniell, C. , “A Fusion System for Real-Time Forward Collision Warning in Automobiles,” in Proceedings of the IEEE International Conference on Intelligent Transportation Systems, 2003.
- Wang, J., Yu, C., Li, S., and Wang, L. , “A Forward Collision Warning Algorithm with Adaptation to Driver Behaviors,” in IEEE Transactions on Intelligent Transportation Systems, 2016.
- Iranmanesh, S.M., Nourkhiz Mahjoub, H., Kazemi, H., and Fallah, Y.P. , “An Adaptive Forward Collision Warning Framework Design Based on Driver Distraction,” IEEE Transactions on Intelligent Transportation Systems, 2018.
- Lin, C., Su, F., Wang, H., and Gao, J. , “A Camera Calibration Method for Obstacle Distance Measurement Based on Monocular Vision,” in International Conference on Communication Systems and Network Technologies, 2014.
- Lu, Y., Yuan, Y., and Wang, Q. , “Forward Vehicle Collision Warning Based on Quick Camera Calibration,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, 2018.
- Ren, S., He, K., Girshick, R.B., and Sun, J. , “Faster {R-CNN:} Towards Real-Time Object Detection with Region Proposal Networks,” CoRR, Vol. abs/1506.01497, 2015.
- Girshick, R., Donahue, J., Darrell, T., and Malik, J. , “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence 38(1):142-158, 2016.
- Girshick, R.B. , “Fast R-CNN,” CoRR, Vol. abs/1504.08083, 2015.
- He, K., Gkioxari, G., Dollár, P. and Girshick, R.B. , “Mask R-CNN,” CoRR, Vol. abs/1703.06870, 2017.
- Krizhevsky, A., Sutskever, I., and Hinton, G.E. , “ImageNet Classification with Deep Convolutional Neural Networks,” . In: Pereira, F.,Burges, C.J.C.,Bottou, L., andWeinberger, K.Q., editors. Advances in Neural Information Processing Systems. 25. (Curran Associates, Inc., 2012), 1097-1105.
- Redmon, J., and Farhadi, A. , “YOLOv3: An Incremental Improvement,” CoRR, Vol. abs/1804.02767, 2018.
- Szegedy, C. et al. , “Going Deeper with Convolutions,” CoRR, 2014.
- He, K., Zhang, X., Ren, S., and Sun, J. , “Deep Residual Learning for Image Recognition,” CoRR, Vol. abs/1512.03385, 2015.
- Simonyan, K. and Zisserman, A. , “Very Deep Convolutional Networks for Large-Scale Image Recognition,” CoRR, Vol. abs/1409.1556, 2014.
- Zyner, A., Worrall, S., Ward, J., and Nebot, E. , “Long Short Term Memory for Driver Intent Prediction,” in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017.
- Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., and Meybodi, M.R. , “Introduction to Learning Automata Models, in Learning Automata Approach for Social Networks, Studies in Computational Intelligence (Springer, 2019).
- Fekri, P., Zadeh, M., and Dargahi, J. , “On the Safety of Autonomous Driving: A Dynamic Deep Object Detection Approach,” SAE Technical Paper 2019-01-1044, 2019, https://doi.org/10.4271/2019-01-1044.