Machine Learning Techniques in ADAS: A Review

What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.

[1]  Hwasoo Yeo,et al.  Real-Time Rear-End Collision-Warning System Using a Multilayer Perceptron Neural Network , 2016, IEEE Transactions on Intelligent Transportation Systems.

[2]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[3]  Kazuya Takeda,et al.  Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification , 2007, Proceedings of the IEEE.

[4]  Qixiang Ye,et al.  Pedestrian Detection with Deep Convolutional Neural Network , 2014, ACCV Workshops.

[5]  Nils J. Nilsson,et al.  Introduction to Machine Learning , 2020, Machine Learning for iOS Developers.

[6]  André Luckow,et al.  2016 Ieee International Conference on Big Data (big Data) Deep Learning in the Automotive Industry: Applications and Tools , 2022 .

[7]  Liming Chen,et al.  Learning-Based Driving Events Recognition and Its Application to Digital Roads , 2015, IEEE Transactions on Intelligent Transportation Systems.

[8]  Jooyoung Park,et al.  Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques , 2017, Sensors.

[9]  Anup Doshi,et al.  Lane change intent prediction for driver assistance: On-road design and evaluation , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[10]  Xin Li,et al.  Reinforcement learning based overtaking decision-making for highway autonomous driving , 2015, 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP).

[11]  Janet Wesson,et al.  Using machine learning to predict the driving context whilst driving , 2013, SAICSIT '13.

[12]  Chunming Liu,et al.  A decision-making method for autonomous vehicles based on simulation and reinforcement learning , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[13]  Fernando A. Mujica,et al.  An Empirical Evaluation of Deep Learning on Highway Driving , 2015, ArXiv.

[14]  Bhiksha Raj,et al.  On the Origin of Deep Learning , 2017, ArXiv.

[15]  Ken Kennedy,et al.  Automotive big data: Applications, workloads and infrastructures , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[16]  Purushottam Kulkarni,et al.  Wolverine: Traffic and road condition estimation using smartphone sensors , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[17]  Praveen Edara,et al.  Modeling Mandatory Lane Changing Using Bayes Classifier and Decision Trees , 2014, IEEE Transactions on Intelligent Transportation Systems.

[18]  Abdelmalek Toumi,et al.  Deep Learning for target recognition from SAR images , 2017, 2017 Seminar on Detection Systems Architectures and Technologies (DAT).

[19]  Vishal Jha Study of Machine Learning Methods in Intelligent Transportation Systems , 2015 .

[20]  Sebastian Thrun,et al.  Assisted Highway Lane Changing with RASCL , 2010, AAAI Spring Symposium: Embedded Reasoning.

[21]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  Ming Zhu,et al.  Obstacle detection in single images with deep neural networks , 2016, Signal Image Video Process..

[24]  Michail G. Lagoudakis,et al.  Least-Squares Policy Iteration , 2003, J. Mach. Learn. Res..

[25]  Xing Xie,et al.  Learning transportation mode from raw gps data for geographic applications on the web , 2008, WWW.

[26]  Hesham A. Rakha,et al.  Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[27]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[28]  Liming Chen,et al.  Learning-based driving events classification , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).