# Interaction Graphs for Object Importance Estimation in On-road Driving Videos

arXiv: https://arxiv.org/abs/2003.06045 · HTML: https://mlsanity.com/paper/2003.06045/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 15 of 15)

- [Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning](https://mlsanity.com/paper/2309.06597/) (2023)
- [Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context](https://mlsanity.com/paper/2301.05804/) (2023)
- [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)
- [Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations](https://mlsanity.com/paper/2305.04516/) (2023)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [Learning Road Scene-level Representations via Semantic Region Prediction](https://mlsanity.com/paper/2301.00714/) (2023)
- [Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification](https://mlsanity.com/paper/2409.15846/) (2024)
- [DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding](https://mlsanity.com/paper/2311.06497/) (2023)
- [On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance](https://mlsanity.com/paper/2411.17152/) (2024)
- [One if by Land, Two if by Sea, Three if by Four Seas, and More to Come -- Values of Perception, Prediction, Communication, and Common Sense in Decision Making](https://mlsanity.com/paper/2601.06077/) (2025)
- [DriveSafe: A Framework for Risk Detection and Safety Suggestions in Driving Scenarios](https://mlsanity.com/paper/2605.16892/) (2026)
- [Self-Supervised Relevance Modelling in Autonomous Driving via Counterfactual Analysis](https://mlsanity.com/paper/2606.10688/) (2026)
- [A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction](https://mlsanity.com/paper/2609.07125/) (2026)

## Abstract (arXiv)

A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions. Learning to estimate the importance of each object on the driver's real-time decision-making may help better understand human driving behavior and lead to more reliable autonomous driving systems. Solving this problem requires models that understand the interactions between the ego-vehicle and the surrounding objects. However, interactions among other objects in the scene can potentially also be very helpful, e.g., a pedestrian beginning to cross the road between the ego-vehicle and the car in front will make the car in front less important. We propose a novel framework for object importance estimation using an interaction graph, in which the features of each object node are updated by interacting with others through graph convolution. Experiments show that our model outperforms state-of-the-art baselines with much less input and pre-processing.