Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding
Open accessAbstract
Grid maps obtained from fused sensory information are nowadays among the most popular approaches for motion planning for autonomous driving cars. In this paper, we introduce Deep Grid Net (DGN), deep learning (DL) system designed for understanding the context in which an autonomous car is driving. DGN incorporates a learned driving environment representation based on Occupancy Grids (OG) obtained from raw Lidar data and constructed on top of the Dempster-Shafer (DS) theory. The predicted driving context is further used for switching between different driving strategies implemented within EB robinos, Elektrobit's Autonomous Driving (AD) software platform. Based on genetic algorithms (GAs), we also propose a neuroevolutionary approach for learning the tuning hyperparameters of DGN. The performance of the proposed deep network has been evaluated against similar competing driving context estimation classifiers.
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Research works printed but not linked: 1
- C. Seeger, A. Mller, and L. Schwarz, "Towards Road Type Classification with Occupancy Grids," in Intelligent Vehicles Symposium -Workshop: DeepDriving -Learning Representations for Intelligent Vehicles, IEEE, Gothenburg, Sweden, July 2016.
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Self-citation
- Authors
- Marina, L. A., Trasnea, B., Cocias, T., Vasilcoi, A., Moldoveanu, F., & Grigorescu, S. M.
- Affiliations
- Transilvania University of Brașov (RO)
- Venue
- 2019 Third IEEE International Conference on Robotic Computing (IRC)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 399-402
- OpenAlex
- W2909626933
- Cited by
- 14
- References
- 17
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2021: 2 · 2020: 3 · 2019: 3
- FWCI
- 1.99
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Robotic Computing (IRC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- autonomous driving, occupancy grid, Dempster-Shafer theory, LiDAR, grid map, neuroevolution, genetic algorithm, hyperparameter optimization, real-time perception
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