Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations

This paper describes the exploration and learnings during the process of developing a self-driving algorithm in simulation, followed by deployment on a real car. We specifically concentrate on the Formula Student Driverless competition. In such competitions, a formula race car, designed and built by students, is challenged to drive through previously unseen tracks that are marked by traffic cones. We explore and highlight the challenges associated with training a deep neural network that uses a single camera as input for inferring car steering angles in real-time. The paper explores in-depth creation of simulation, usage of simulations to train and validate the software stack and then finally the engineering challenges associated with the deployment of the system in real-world.

[1]  Renaud Dubé,et al.  Design of an Autonomous Racecar: Perception, State Estimation and System Integration , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Yevgen Chebotar,et al.  Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[3]  Ashish Kapoor,et al.  AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles , 2017, FSR.

[4]  Skanda Koppula.  Learning a CNN-based End-to-End Controller for a Formula SAE Racecar , 2017, ArXiv.

[5]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Sebastian Thrun,et al.  Robust vehicle localization in urban environments using probabilistic maps , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Victor Talpaert,et al.  Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps , 2018, ECCV Workshops.

[9]  Andreas Geiger,et al.  Map-Based Probabilistic Visual Self-Localization , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[11]  Andrew V. Goldberg,et al.  Route Planning in Transportation Networks , 2015, Algorithm Engineering.

[12]  Sergey Levine,et al.  Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Shaojie Shen,et al.  Stereo R-CNN Based 3D Object Detection for Autonomous Driving , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[15]  Atil Iscen,et al.  Sim-to-Real: Learning Agile Locomotion For Quadruped Robots , 2018, Robotics: Science and Systems.

[16]  Marcin Andrychowicz,et al.  Sim-to-Real Transfer of Robotic Control with Dynamics Randomization , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Xin Zhang,et al.  End to End Learning for Self-Driving Cars , 2016, ArXiv.

[18]  Ashish Mehta,et al.  Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision , 2018, ICVGIP.

[19]  Alexey Dosovitskiy,et al.  End-to-End Driving Via Conditional Imitation Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Vijay John,et al.  3D point cloud map based vehicle localization using stereo camera , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[21]  Liran Gispan,et al.  Closing the gap towards end-to-end autonomous vehicle system , 2019, ArXiv.

[22]  Cewu Lu,et al.  Virtual to Real Reinforcement Learning for Autonomous Driving , 2017, BMVC.