This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Implementation and Validation of Behavior Cloning Using Scaled Vehicles
Technical Paper
2021-01-0248
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
Recent trends in autonomy have emphasized end-to-end deep-learning-based methods that have shown a lot of promise in overcoming the requirements and limitations of feature-engineering. However, while promising, the black-box nature of deep-learning frameworks now exacerbates the need for testing with end-to-end deployments. Further, as exemplars of systems-of-systems, autonomous vehicles (AVs) engender numerous interconnected component-, subsystem and system-level interactions. The ensuing complexity creates challenges for verification and validation at the various component, subsystem- and system-levels as well as end-to-end testing. While simulation-based testing is one promising avenue, oftentimes the lack of adequate fidelity of AV and environmental modeling limits the generalizability. In contrast, full-scale AV testing presents the usual limitations of time-, space-, and cost. Hence in this paper, we explore the opportunity for using experiential learning possible with a scaled vehicle-based deployment to overcome the limitations(e.g. simulation fidelity or experimentation costs) of scaled vehicles to lower the barriers especially at the early stages of testing of autonomy algorithms.
In recent times, several efforts have emerged for testing deep-learning-based autonomy algorithms on scaled vehicles - the Nvidia Jet racer, Amazon Deep racer, and Donkey car are being widely used. In this paper, we examine a deployment of the Donkey car Behavior Cloning software stack on a 1/10th scaled vehicle (F1tenth) and the issues faced while deploying the other software stacks. In particular, we explored the effectiveness of: (i) mixing and matching frameworks; and (ii) use of scaled vehicles in an academic set up to support testing and deployment of supervised learning (behavior cloning) technique to achieve lane-keeping and obstacle-avoidance. We showcase that the use of this scaled-vehicle framework permitted the rapid exploration of many different test tracks (challenging with full-scale vehicle tests) while retaining realistic environmental conditions (challenging with simulation-alone testing).
Authors
Topic
Citation
Verma, A., Bagkar, S., Allam, N., Raman, A. et al., "Implementation and Validation of Behavior Cloning Using Scaled Vehicles," SAE Technical Paper 2021-01-0248, 2021, https://doi.org/10.4271/2021-01-0248.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 | ||
Unnamed Dataset 3 |
Also In
References
- Kanervisto , A. , Pussinen , J. , and Hautamäki , V. 2020
- Codevilla , F. , Santana , E. , López , A.M. , and Gaidon , A. Exploring the Limitations of Behavior Cloning for Autonomous Driving Proceedings of the IEEE International Conference on Computer Vision 2019 9329 9338
- Du , W. and Ji , Y. 2019
- Bojarski , M. et al. 2016
- Bulsara , A. , Raman , A. , Kamarajugadda , S. , Schmid , M. , and Krovi , V.N. Obstacle Avoidance Using Model Predictive Control: An Implementation and Validation Study Using Scaled Vehicles SAE Technical Paper 2020-01-0109 2020 https://doi.org/10.4271/2020-01-0109
- Agnihotri , A. , O’Kelly , M. , Mangharam , R. , and Abbas , H. 2020
- Ivanov , R. , Carpenter , T.J. , Weimer , J. , Alur , R. , Pappas , G.J. , and Lee , I. Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control 2020 1 7
- O’Kelly , M. , Zheng , H. , Jain , A. , Auckley , J. , Luong , K. , and Mangharam , R. TunerCar: A Superoptimization Toolchain for Autonomous Racing 2020 IEEE International Conference on Robotics and Automation (ICRA) 2020 IEEE 5356 5362
- Roza , F. March 30, 2019
- Chen , Z. and Huang , X. End-to-End Learning for Lane Keeping of Self-Driving Cars 2017 IEEE Intelligent Vehicles Symposium (IV) 2017 IEEE 1856 1860
- Schwarting , W. , Alonso-Mora , J. , and Rus , D. Planning and Decision-Making for Autonomous Vehicles Annual Review of Control, Robotics, and Autonomous Systems 2018
- Vishnukumar , H.J. , Butting , B. , Müller , C. , and Sax , E. Machine Learning and Deep Neural Network—Artificial Intelligence Core for Lab and Real-World Test and Validation for ADAS and Autonomous vehicles: AI for Efficient and Quality Test and Validation 2017 Intelligent Systems Conference (IntelliSys) 2017 IEEE 714 721
- Mozaffari , S. , Al-Jarrah , O.Y. , Dianati , M. , Jennings , P. , and Mouzakitis , A. Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review IEEE Transactions on Intelligent Transportation Systems 2020
- Shapiro , D. Accelerating the Race to Autonomous Cars Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2016 415 415
- Wayve April 3rd, 2019
- Moujahid , A. et al. Machine Learning Techniques in ADAS: A Review 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE) 2018 IEEE 235 242
- Kalra , N. and Paddock , S.M. Driving to Safety: How Many Miles of Driving Would it Take to Demonstrate Autonomous Vehicle Reliability? Transportation Research Part A: Policy and Practice 94 182 193 2016
- Seshia , S.A. , Sadigh , D. , and Sastry , S.S. 2016
- Li , R. , Wang , W. , Chen , Y. , Srinivasan , S. , and Krovi , V.N. An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles Dynamic Systems and Control Conference 2018 51906 American Society of Mechanical Engineers V002T15A004
- McCreary , D.
- O’Kelly , M. et al. 2019
- Zhang , Q. , Du , T. , and Tian , C. 2019
- Intisar , A. , Islam , M.K.B. , and Rahman , J. A Deep Convolutional Neural Network Based Small Scale Test-Bed for Autonomous Car Proceedings of the International Conference on Computing Advancements 2020 1 5
- 2020
- Selby , W. Dec 9, 2019
- Jabbar , H. and Khan , R.Z. Methods to Avoid Over-Fitting and Under-Fitting in Supervised Machine Learning (Comparative Study) Computer Science, Communication and Instrumentation Devices 163 172 2015
- Srivastava , N. , Hinton , G. , Krizhevsky , A. , Sutskever , I. , and Salakhutdinov , R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting The Journal of Machine Learning Research 15 1 1929 1958 2014
- Swalin , A. 2018
- Mishra , A. Metrics to Evaluate Your Machine Learning Algorithm Towards Data Science 2018