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We present an implementation of a formally verified safety fallback controller for improved collision avoidance in an autonomous vehicle research platform. Our approach uses a primary trajectory planning system that aims for collision-free navigation in the presence of pedestrians and other vehicles, and a fallback controller that guards its behavior. The safety fallback controller excludes the possibility of collisions by accounting for nondeterministic uncertainty in the dynamics of the vehicle and moving obstacles, and takes over the primary controller as necessary. We demonstrate the system in an experimental set-up that includes simulations and real-world tests with a 1/5-scale vehicle. In stressing simulation scenarios, the safety fallback controller significantly reduces the number of collisions.more » « less
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Sefati, Shahriar ; Mishra, Subhransu ; Sheckells, Matthew ; Katyal, Kapil D. ; Bai, Jin ; Hager, Gregory D. ; Kobilarov, Marin ( , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
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Sheckells, Matthew ; Garimella, Gowtham ; Mishra, Subhransu ; Kobilarov, Marin ( , ICRA 2019)This work develops a technique for using robot motion trajectories to create a high quality stochastic dynamics model that is then leveraged in simulation to train control policies with associated performance guarantees. We demonstrate the idea by collecting dynamics data from a 1/5 scale agile ground vehicle, fitting a stochastic dynamics model, and training a policy in simulation to drive around an oval track at up to 6.5 m/s while avoiding obstacles. We show that the control policy can be transferred back to the real vehicle with little loss in predicted performance. We compare this to an approach that uses a simple analytic car model to train a policy in simulation and show that using a model with stochasticity learned from data leads to higher performance in terms of trajectory tracking accuracy and collision probability. Furthermore, we show empirically that simulation-derived performance guarantees transfer to the actual vehicle when executing a policy optimized using a deep stochastic dynamics model fit to vehicle data.more » « less