26,808 research outputs found
Benchmarking of a software stack for autonomous racing against a professional human race driver
The way to full autonomy of public road vehicles requires the step-by-step
replacement of the human driver, with the ultimate goal of replacing the driver
completely. Eventually, the driving software has to be able to handle all
situations that occur on its own, even emergency situations. These particular
situations require extreme combined braking and steering actions at the limits
of handling to avoid an accident or to diminish its consequences. An average
human driver is not trained to handle such extreme and rarely occurring
situations and therefore often fails to do so. However, professional race
drivers are trained to drive a vehicle utilizing the maximum amount of possible
tire forces. These abilities are of high interest for the development of
autonomous driving software. Here, we compare a professional race driver and
our software stack developed for autonomous racing with data analysis
techniques established in motorsports. The goal of this research is to derive
indications for further improvement of the performance of our software and to
identify areas where it still fails to meet the performance level of the human
race driver. Our results are used to extend our software's capabilities and
also to incorporate our findings into the research and development of public
road autonomous vehicles.Comment: Accepted at 2020 Fifteenth International Conference on Ecological
Vehicles and Renewable Energies (EVER
Diagnosis and Repair for Synthesis from Signal Temporal Logic Specifications
We address the problem of diagnosing and repairing specifications for hybrid
systems formalized in signal temporal logic (STL). Our focus is on the setting
of automatic synthesis of controllers in a model predictive control (MPC)
framework. We build on recent approaches that reduce the controller synthesis
problem to solving one or more mixed integer linear programs (MILPs), where
infeasibility of a MILP usually indicates unrealizability of the controller
synthesis problem. Given an infeasible STL synthesis problem, we present
algorithms that provide feedback on the reasons for unrealizability, and
suggestions for making it realizable. Our algorithms are sound and complete,
i.e., they provide a correct diagnosis, and always terminate with a non-trivial
specification that is feasible using the chosen synthesis method, when such a
solution exists. We demonstrate the effectiveness of our approach on the
synthesis of controllers for various cyber-physical systems, including an
autonomous driving application and an aircraft electric power system
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