# Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey arXiv: https://arxiv.org/abs/2501.01886 ยท HTML: https://mlsanity.com/paper/2501.01886/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. - Builds on 42 of its 174 references that are in the map. - First posted on arXiv 3 Jan 2025. ## Placed in - End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/) ## Builds on (most cited first, 30 of 42) - [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018) - [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018) - [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023) - [Interpretation of Neural Networks is Fragile](https://mlsanity.com/paper/1710.10547/) (2017) - [Bottleneck Transformers for Visual Recognition](https://mlsanity.com/paper/2101.11605/) (2021) - [ResMLP: Feedforward networks for image classification with data-efficient training](https://mlsanity.com/paper/2105.03404/) (2021) - [DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving](https://mlsanity.com/paper/2312.09245/) (2023) - [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019) - [Sanity Checks for Saliency Metrics](https://mlsanity.com/paper/1912.01451/) (2019) - [Model-free Deep Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/1904.09503/) (2019) - [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021) - [Beyond Sparsity: Tree Regularization of Deep Models for Interpretability](https://mlsanity.com/paper/1711.06178/) (2017) - [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024) - [Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic](https://mlsanity.com/paper/2105.05701/) (2021) - [A Survey on Automated Driving System Testing: Landscapes and Trends](https://mlsanity.com/paper/2206.05961/) (2022) - [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022) - [OCTET: Object-aware Counterfactual Explanations](https://mlsanity.com/paper/2211.12380/) (2022) - [Autonomous Highway Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.00035/) (2019) - [Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving](https://mlsanity.com/paper/1910.12905/) (2019) - [Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic](https://mlsanity.com/paper/1906.11021/) (2019) - [Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks](https://mlsanity.com/paper/1910.12370/) (2019) - [Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge](https://mlsanity.com/paper/1709.02066/) (2017) - [Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections](https://mlsanity.com/paper/2201.07833/) (2022) - [Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections](https://mlsanity.com/paper/2006.09786/) (2020) - [Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving](https://mlsanity.com/paper/2103.13861/) (2021) - [Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory](https://mlsanity.com/paper/2003.11071/) (2020) - [SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving](https://mlsanity.com/paper/2305.06940/) (2023) - [A Rule-Based Behaviour Planner for Autonomous Driving](https://mlsanity.com/paper/2407.00460/) (2024) - [Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.08691/) (2020) - [NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets](https://mlsanity.com/paper/2012.10547/) (2020) ## Built on by (most cited first, 1 of 1) - [Context-Aware Multi-Turn Visual-Textual Reasoning in LVLMs via Dynamic Memory and Adaptive Visual Guidance](https://mlsanity.com/paper/2509.05669/) (2025) ## Abstract (arXiv) Autonomous vehicles (AVs) can significantly promote the advances in road transport mobility in terms of safety, reliability, and decarbonization. However, ensuring safety and efficiency in interactive during within dynamic and diverse environments is still a primary barrier to large-scale AV adoption. In recent years, deep reinforcement learning (DRL) has emerged as an advanced AI-based approach, enabling AVs to learn decision-making strategies adaptively from data and interactions. DRL strategies are better suited than traditional rule-based methods for handling complex, dynamic, and unpredictable driving environments due to their adaptivity. However, varying driving scenarios present distinct challenges, such as avoiding obstacles on highways and reaching specific exits at intersections, requiring different scenario-specific decision-making algorithms. Many DRL algorithms have been proposed in interactive decision-making. However, a rationale review of these DRL algorithms across various scenarios is lacking. Therefore, a comprehensive evaluation is essential to assess these algorithms from multiple perspectives, including those of vehicle users and vehicle manufacturers. This survey reviews the application of DRL algorithms in autonomous driving across typical scenarios, summarizing road features and recent advancements. The scenarios include highways, on-ramp merging, roundabouts, and unsignalized intersections. Furthermore, DRL-based algorithms are evaluated based on five rationale criteria: driving safety, driving efficiency, training efficiency, unselfishness, and interpretability (DDTUI). Each criterion of DDTUI is specifically analyzed in relation to the reviewed algorithms. Finally, the challenges for future DRL-based decision-making algorithms are summarized.