Promising new technology has recently emerged to increase the level of safety and autonomy in driving, including lane and distance keeping assist systems, automatic braking systems, and even highway auto-drive systems. Each of these technologies brings cars closer to the ultimate goal of fully autonomous operation. While it is still unclear, if and when safe, driverless cares will be released on the mass market, a comparison with the development of aircraft autopilot systems can provide valuable insight. This review article contains several Additional Resources at the end, including key references to support its findings. The article investigates a path towards ensuring safety for "self-driving" or "autonomous" cars by leveraging prior work in aviation. It focuses on navigation, or localization, which is a key aspect of automated operation.
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Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem
The engineering community currently encounters significant challenges in the development of intelligent transportation algorithms that can be transferred from simulation to reality with minimal effort. This can be achieved by robustifying the algorithms using domain adaptation methods and/or by adopting cutting-edge tools that help support this objective seamlessly. This work presents AutoDRIVE, an openly accessible digital twin ecosystem designed to facilitate synergistic development, simulation and deployment of cyber-physical solutions pertaining to autonomous driving technology; and focuses on bridging the autonomy-oriented simulation-to-reality (sim2real) gap using the proposed ecosystem. In this paper, we extensively explore the modeling and simulation aspects of the ecosystem and substantiate its efficacy by demonstrating the successful transition of two candidate autonomy algorithms from simulation to reality to help support our claims: (i) autonomous parking using probabilistic robotics approach; (ii) behavioral cloning using deep imitation learning. The outcomes of these case studies further strengthen the credibility of AutoDRIVE as an invaluable tool for advancing the state-of-the-art in autonomous driving technology.
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- NSF-PAR ID:
- 10491342
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- IFAC-PapersOnLine
- Volume:
- 56
- Issue:
- 3
- ISSN:
- 2405-8963
- Page Range / eLocation ID:
- 277 to 282
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ifacol.2023.12.037
