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20212025
most citedExplainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review

130 citations · 203 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CY2025★ 32 cited

AI Safety for Everyone

Balint Gyevnar, Atoosa Kasirzadeh

Recent discussions and research in AI safety have increasingly emphasized the deep connection between AI safety and existential risk from advanced AI systems, suggesting that work…

cs.RO2024★ 130 cited

Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review

Anton Kuznietsov, Balint Gyevnar, Cheng Wang +2

Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional…

cs.AI2023★ 2 cited

Causal Explanations for Sequential Decision-Making in Multi-Agent Systems

Balint Gyevnar, Cheng Wang, Christopher G. Lucas +2

We present CEMA: Causal Explanations in Multi-Agent systems; a framework for creating causal natural language explanations of an agent's decisions in dynamic sequential multi-agent…

cs.AI2023★ 35 cited

Bridging the Transparency Gap: What Can Explainable AI Learn From the AI Act?

Balint Gyevnar, Nick Ferguson, Burkhard Schafer

The European Union has proposed the Artificial Intelligence Act which introduces detailed requirements of transparency for AI systems. Many of these requirements can be addressed b…

cs.MA2022★ 1 cited

Deep Reinforcement Learning for Multi-Agent Interaction

Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho +14

The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Tow…

cs.RO2022★ 3 cited

A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning

Balint Gyevnar, Massimiliano Tamborski, Cheng Wang +3

Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and q…

cs.RO2021

GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving

Cillian Brewitt, Balint Gyevnar, Samuel Garcin +1

It is important for autonomous vehicles to have the ability to infer the goals of other vehicles (goal recognition), in order to safely interact with other vehicles and predict the…