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- Daza, Iván García, et al.
(author)
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Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving
- 2023
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In: Applied Intelligence. - : Springer Science and Business Media LLC. - 1573-7497 .- 0924-669X. ; 53:10, s. 12719-12735
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Journal article (peer-reviewed)abstract
- The main challenge for the adoption of autonomous driving is to ensure an adequate level of safety. Considering the almost infinite variability of possible scenarios that autonomous vehicles would have to face, the use of autonomous driving simulators is becoming of utmost importance. Simulation suites allow the used of automated validation techniques in a wide variety of scenarios, and enable the development of closed-loop validation methods, such as machine learning and reinforcement learning approaches. However, simulation tools suffer from a standing flaw in that there is a noticeable gap between the simulation conditions and real-world scenarios. Although the use of simulators powers most of the research around autonomous driving, and is generally used within all domains it is divided into, there is an inherent source of error given the stochastic nature of activities performed in real world, which are unreplicable in computer environments. This paper proposes a new approach to assess the real-to-sim gap for path tracking systems. The aim is to narrow down the sources of error between simulation results and real-world conditions, and to evaluate the performance of the simulation suite in the design process by employing the information extracted from gap analysis, which adds a new dimension of development against other approaches for autonomous driving. A real-time model predictive controller (MPC) based on adaptive potential fields was developed and validated using the CARLA simulator. Both the path planning and vehicle control systems where tested in real traffic conditions. The error between the simulator and the real data acquisition was evaluated using the Pearson correlation coefficient (PCC) and the max normalized cross-correlation (MNCC). The controller was further evaluated on a process of sim-to-real transfer, and was finally tested both in simulation and real traffic conditions. A comparison was performed against an optimal-control ILQR-based model predictive controller was carried out to further showcase the validity of this approach.
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- Engström, Artur, et al.
(author)
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A lidar-only SLAM algorithm for marine vessels and autonomous surface vehicles
- 2022
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In: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 55:31, s. 229-234
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Conference paper (peer-reviewed)abstract
- Research into autonomous surface vehicles is noticeably limited in regards to the functionality of the vehicles themselves. Specifically, testing and evaluation typically occurs at speeds considerably lower than what is allowed in an operational setting. For a vessel to be able to take advantage of higher speeds, there must be a robust and tested method for determining localisation and navigation. With an emphasis of development for small vessels with higher impulse capabilities, working in confined and restricted environments, the decision was made to develop a method of navigation that relied solely upon lightweight sensors. For this, a single light ranging sensor was utilised to develop both simultaneous localisation and mapping for the vessel, using the normal distribution transform and iterative closest point methods. Evaluation of the algorithm accuracy as the vessel moved above speeds greater than two metres per second was conducted, and it was feasibly evaluated that there was no observable drift of mapping in horizontal planes, however, there was a accumulated drift in the vertical plane and a transient response in localisation deviation as the vessel changed impulse through the two metre per second window.
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