Autonomous Vehicle Using Various Machine Learning Algorithms
DOI:
https://doi.org/10.32628/CSEIT1952205Keywords:
Self-driving car, Regression, Clustering, Decision Matrix, Pattern Recognition, Reinforcement Learning, Markov Decision Process, Q-LearningAbstract
Today and possibly for a long time to come, the full driving task is too complex an activity to be fully formalized as a sensing-acting robotics system that can be explicitly solved through model based and learning-based approaches in order to achieve full unconstrained vehicles autonomy. Vehicle control, mapping, scene perception, trajectory optimization, and higher-level planning decisions are open challenges for autonomous vehicle development. This is especially true for real-world operation where the margin of allowable error is extremely small and the number of edge-cases is extremely large. Human beings will remain an integral part of monitoring the AI system as it performs anywhere from just over 0% to just under 100% of the driving until we solved these problems. We are continually developing new methods for analysis of the massive-scale dataset collected from various vehicle owners. Many recorded data has various messages, and high-de?nition video streams of the driver face, the driver cabin, the forward roadway, and the instrument cluster. The study is on- going and growing. Till date, we have 110 participants, 11,945 days of participation, 89,405,807 miles, and 10.9 billion video frames. Here we presents the design of the study, the data collection hardware, the processing of the data, and the computer vision algorithms currently being used to extract actionable knowledge from the data.
References
- A. Davies, "Oh Look, More Evidence Humans Shouldn't Be Driving," May 2015. Online]. Available: Https://Www.Wired.Com/ 2015/05/Oh-Look-Evidence-Humans-Shouldn't-Driving/
- T. Vanderbilt And B. Brenner, "Traffic: Why We Drive The Way We Do (And What It Says About Us) , Alfred A. Knopf, New York, 2008; 978-0307-26478-7," 2009.
- W. H. Organization, Global Status Report On Road Safety 2015. World Health Organization, 2015.
- M. Buehler, K. Iagnemma, And S. Singh, The Darpa Urban Challenge: Autonomous Vehicles In City Traffic. Springer, 2009, Vol. 56.
- V. V. Dixit, S. Chand, And D. J. Nair, "Autonomous Vehicles: Disengagements, Accidents And Reaction Times," Plos One, Vol. 11, No. 12, P. E0168054, 2016.
- F. M. Favar`O, N. Nader, S. O. Eurich, M. Tripp, And N. Varadaraju, "Examining Accident Reports Involving Autonomous Vehicles In California," Plos One, Vol. 12, No. 9, P. E0184952, 2017.
- R. Tedrake, "Underactuated Robotics: Algorithms For Walking, Running, Swimming, flying, And Manipulation (Course Notes For Mit 6.832)," 2016.
- M. R. Endsley And E. O. Kiris, "The Out-Of-The-Loop Performance Problem And Level Of Control In Automation," Human Factors, Vol. 37, No. 2, Pp. 381–394, 1995.
- Nello Cristianini And John Shawe-Taylor, An Introduction To Support Vector Machines And Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
- Girish Kumar Jha, "Artificial Neural Network And Its Applications", Iari New Delhi.
- I. Goodfellow, Y. Bengio, And A. Courville, Deep Learning ,Mit Press 2016.
- Girish Kumar Jha, "Artificial Neural Network And Its Applications", Iari New Delhi.
- M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang Et Al., "End To End Learning For Self-Driving Cars," Arxiv Preprint Arxiv:1604.07316, 2016.
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