Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving
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When AI systems help us in areas such as hiring, financial risk assessment, and face identification, systematic unfairness in their decisions might have negative social ramifications.
“When AI systems help us in areas such as hiring, financial risk assessment, and face identification, systematic unfairness in their decisions might have negative social ramifications (e.g., underprivileged groups might experience systematic disadvantage in hiring decisions [48], or be disproportionally impacted in criminal risk profiling [104, 161, 146]).”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [48]
Many current AI systems are biased against underrepresented groups.
“many current AI systems are found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [48]
DNN's lack of explainability places a demand to require explicit explanations or impose rules to ensure that the vehicle is behaving in a regulated manner.
“At the algorithm level, though DNN has achieved impressive performance in autonomous driving tasks like perception and planning, its lack of explainability still places a demand to require explicit explanations or impose rules to ensure that the vehicle is behaving in a regulated manner [96, 48].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [48]
Cites
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Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · see citing works
Markov decision processes: Discrete stochastic dynamic programming
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Deep Reinforcement Learning with Double Q-Learning
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An introduction to deep reinforcement learning
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · doi:10.1561/9781680835397 · Cited by 544 · see citing works
Virtual to Real Reinforcement Learning for Autonomous Driving
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End-to-End Driving Via Conditional Imitation Learning
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Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612 · see citing works
- Authors
- Likmeta, A., Metelli, A. M., Tirinzoni, A., Giol, R., Restelli, M., & Romano, D.
- Affiliations
- Politecnico di Milano (IT)
- Corresponding author
- Likmeta, Amarildo
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 131
- Pages
- 103568
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W3037429136
- Cited by
- 106
- References
- 10
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 22 · 2023: 21 · 2022: 16 · 2021: 8
- FWCI
- 4.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Robotics and Autonomous Systems (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- autonomous driving, high-level decision making, reinforcement learning, rule-based controller, explainable decision-making, transparency, robustness, highway driving, intersection driving, urban driving conditions
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