A Parallel Autonomy System: Data-Driven and Model-Based Parallel Autonomy with Robustness and Safety Guarantees

Parallel Autonomy System
Daniela Rus, Sertac Karaman

This project will develop a parallel autonomy system to create a collision-proof car. We will instrument a Toyota vehicle with a suite of sensors pointed at the environment and at the driver to create situational awareness, both inside and outside the vehicle. We will develop and integrate the perception and decision- making software components to implement the parallel autonomy software core. We will also develop and implement novel algorithms that take control of the vehicle in dangerous situations to prevent accidents. We will evaluate the system both in simulation and in testing on a closed-course near MIT. We will increase the difficulty of our tests over time, moving from lower to higher speeds and from low to high environmental complexity. We will improve the system over time, following a modular design in which we continually update critical system components with improved algorithms for perception, motion planning and control.

This is a continuation of the project "A Parallel Autonomous Driving System" by Daniela Rus, John Leonard, Sertac Karaman.


  1. Xiao Li, Guy Rosman, Igor Gilitschenski, Jon DeCastro, Cristi-Ioan Vasile, Sertac Karaman, and Daniela Rus, “Differentiable Logic Layer for Rule Guided Trajectory Prediction,” in CoRL 2020 (accepted), 2020.
  2. Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, and Daniela Rus, “Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space,” in CoRL 2020 (accepted), 2020.
  3. Alexander Amini, Wilko Schwarting, Ava Soleimany, Sertac Karaman, and Daniela Rus, “Deep Evdential Regression,” in NeurIPS 2020 (accepted), 2020.
  4. I. Gilitschenski, G. Rosman, A. Gupta, S. Karaman, and D. Rus, “Deep Context Maps: Agent Trajectory Prediction Using Location-Specific Latent Maps,” IEEE Robot. Autom. Lett., vol. 5, no. 4, pp. 5097–5104, Oct. 2020, doi: 10.1109/LRA.2020.3004800. [Online]. Available: https://doi.org/10.1109/LRA.2020.3004800
  5. T. Ort, K. Murthy, R. Banerjee, S. K. Gottipati, D. Bhatt, I. Gilitschenski, L. Paull, and D. Rus, “MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 556–563, Apr. 2020, doi: 10.1109/LRA.2019.2961051. [Online]. Available: https://doi.org/10.1109/LRA.2019.2961051
  6. A. Amini, I. Gilitschenski, J. Phillips, J. Moseyko, R. Banerjee, S. Karaman, and D. Rus, “Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 1143–1150, Apr. 2020, doi: 10.1109/LRA.2020.2966414. [Online]. Available: https://doi.org/10.1109/LRA.2020.2966414
  7. T. Seyde, W. Schwarting, S. Karaman, and D. Rus, “Learning to Plan via Deep Optimistic Value Exploration,” Proceedings of Machine Learning Research, vol. 120, pp. 1–11, 2020 [Online]. Available: http://proceedings.mlr.press/v120/seyde20a.html
  8. I. Gilitschenski, R. Sahoo, W. Schwarting, A. Amini, S. Karaman, and D. Rus, “DEEP ORIENTATION UNCERTAINTY LEARNING BASED ON A BINGHAM LOSS,” in ICLR 2020, 2020 [Online]. Available: https://iclr.cc/virtual_2020/poster_ryloogSKDS.html
  9. M. Abu-Khalaf, S. Karaman, and D. Rus, “Shared Linear Quadratic Regulation Control: A Reinforcement Learning Approach,” in 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France, 2019, pp. 4569–4576, doi: 10.1109/CDC40024.2019.9029617 [Online]. Available: https://doi.org/10.1109/CDC40024.2019.9029617
  10. F. Naser, I. Gilitschenski, A. Amini, C. Liao, G. Rosman, S. Karaman, and D. Rus, “Infrastructure-free NLoS Obstacle Detection for Autonomous Cars,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 250–257, doi: 10.1109/IROS40897.2019.8967554 [Online]. Available: https://doi.org/10.1109/IROS40897.2019.8967554
  11. X. Du, M. H. A. Jr, S. Karaman, and D. Rus, “A Unified Pipeline for 3D Detection and Velocity Estimation of Vehicles,” in ISRR 2019, 2019 [Online]. Available: https://ras.papercept.net/conferences/conferences/ISRR19/program/ISRR19_ContentListWeb_3.html
  12. S. Gumussoy and M. Abu-Khalaf, “Analytic Solution of a Delay Differential Equation Arising in Cost Functionals for Systems with Distributed Delays,” IEEE Transactions on Automatic Control, Jun. 2019 [Online]. Available: https://doi.org/10.1109/TAC.2019.2921658
  13. B. Araki, I. Gilitschenski, T. Ogata, A. Wallar, W. Schwarting, Z. Choudhury, S. Karaman, and D. Rus, “Range-based Cooperative Localization with Nonlinear Observability Analysis,” in The 22nd Intelligent Transportation Systems Conference (ITSC2019), 2019 [Online]. Available: https://www.itsc2019.org
  14. A. Amini, G. Rosman, S. Karaman, and D. Rus, “Variational End-to-End Navigation and Localization,” in 2019 International Conference on Robotics and Automation (ICRA), 2019 [Online]. Available: https://doi.org/10.1109/ICRA.2019.8793579
  15. F. M. Naser, “Detection of Dynamic Obstacles out of the Line of Sight for Autonomous Vehicles to increase Safety based on Shadows,” 2019 [Online]. Available: https://dspace.mit.edu/handle/1721.1/121657
  16. F. Naser, I. Gilitschenski, G. Rosman, A. Amini, F. Durand, A. Torralba, G. W. Wornell, W. T. Freeman, S. Karaman, and D. Rus, “ShadowCam: Real-Time Detection of Moving Obstacles Behind A Corner For Autonomous Vehicles,” in 21st IEEE International Conference on Intelligent Transportation Systems (ITSC 2018), 2018 [Online]. Available: https://doi.org/10.1109/ITSC.2018.8569569
  17. L. Liebenwein, C. Baykal, I. Gilitschenski, S. Karaman, and D. Rus, “Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees,” in Robotics: Science and Systems, 2018 [Online]. Available: http://www.roboticsproceedings.org/rss14/p14.pdf
  18. A. Amini, L. Paull, Thomas Balch, Sertac Karaman, and D. Rus, “Learning Steering Bounds for Parallel Autonomous Systems,” in ICRA 2018, 2018 [Online]. Available: https://doi.org/10.1109/ICRA.2018.8461253
  19. A. Amini, W. Schwarting, G. Rosman, B. Araki, S. Karaman, and D. Rus, “Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing,” in IROS 2018, 2018 [Online]. Available: https://doi.org/10.1109/IROS.2018.8594386
  20. A. Amini, A. Soleimany, S. Karaman, and D. Rus, “Spatial Uncertainty Sampling for End to End Control,” in Bayesian Deep Learning, NIPS 2017 Workshop, Long Beach, CA, US, 2017 [Online]. Available: http://bayesiandeeplearning.org/2017/papers/25.pdf
  21. W. Schwarting, J. Alonso-Mora, L. Paull, S. Karaman, and D. Rus, “Safe Nonlinear Trajectory Generation for Parallel Autonomy with a Dynamic Vehicle Model,” IEEE Transactions on Intelligent Transportation Systems, Oct. 2017 [Online]. Available: https://doi.org/10.1109/TITS.2017.2771351
  22. W. Schwarting, J. Alonso-Mora, L. Paull, S. Karaman, and D. Rus, “Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore, 2017 [Online]. Available: https://doi.org/10.1109/ICRA.2017.7989224