# Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference

arXiv: https://arxiv.org/abs/2003.02425 · HTML: https://mlsanity.com/paper/2003.02425/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 30 of 34)

- [DRAMA: Joint Risk Localization and Captioning in Driving](https://mlsanity.com/paper/2209.10767/) (2022)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning](https://mlsanity.com/paper/2309.06597/) (2023)
- [HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving](https://mlsanity.com/paper/2309.05186/) (2023)
- [Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process](https://mlsanity.com/paper/2202.10589/) (2022)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance](https://mlsanity.com/paper/2209.12386/) (2022)
- [Explaining Autonomous Driving Actions with Visual Question Answering](https://mlsanity.com/paper/2307.10408/) (2023)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset](https://mlsanity.com/paper/2112.00942/) (2021)
- [Important Object Identification with Semi-Supervised Learning for Autonomous Driving](https://mlsanity.com/paper/2203.02634/) (2022)
- [RiskBench: A Scenario-based Benchmark for Risk Identification](https://mlsanity.com/paper/2312.01659/) (2023)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving](https://mlsanity.com/paper/2403.12176/) (2024)
- [Exploring the Causality of End-to-End Autonomous Driving](https://mlsanity.com/paper/2407.06546/) (2024)
- [Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving](https://mlsanity.com/paper/2102.11905/) (2021)
- [Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour](https://mlsanity.com/paper/2302.00064/) (2023)
- [An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos](https://mlsanity.com/paper/2209.07922/) (2022)
- [Learning Road Scene-level Representations via Semantic Region Prediction](https://mlsanity.com/paper/2301.00714/) (2023)
- [Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour](https://mlsanity.com/paper/2306.03354/) (2023)
- [Gated Driver Attention Predictor](https://mlsanity.com/paper/2308.02530/) (2023)
- [Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification](https://mlsanity.com/paper/2409.15846/) (2024)
- [ATARS: An Aerial Traffic Atomic Activity Recognition and Temporal Segmentation Dataset](https://mlsanity.com/paper/2503.18553/) (2025)
- [DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy](https://mlsanity.com/paper/2506.16720/) (2025)
- [CaTFormer: Causal Temporal Transformer with Dynamic Contextual Fusion for Driving Intention Prediction](https://mlsanity.com/paper/2507.13425/) (2025)
- [Towards Driver Behavior Understanding: Weakly-Supervised Risk Perception in Driving Scenes](https://mlsanity.com/paper/2603.05926/) (2026)
- [DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding](https://mlsanity.com/paper/2311.06497/) (2023)
- [Context-Aware Quantitative Risk Assessment Machine Learning Model for Drivers Distraction](https://mlsanity.com/paper/2402.13421/) (2024)

## Abstract (arXiv)

A significant amount of people die in road accidents due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in an urgent need. Risky situations are generally defined based on collision prediction in the existing works. However, collision is only a source of potential risks, and a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., objects influencing drivers' behavior are risky. A new task called risk object identification is introduced. We formulate the task as the cause-effect problem and present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model. We demonstrate favorable performance on risk object identification compared with strong baselines on the Honda Research Institute Driving Dataset (HDD). Our framework achieves a substantial average performance boost over a strong baseline by 7.5%.