# A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction

arXiv: https://arxiv.org/abs/2609.07125 · HTML: https://mlsanity.com/paper/2609.07125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [Talk2Car: Taking Control of Your Self-Driving Car](https://mlsanity.com/paper/1909.10838/) (2019)
- [DRAMA: Joint Risk Localization and Captioning in Driving](https://mlsanity.com/paper/2209.10767/) (2022)
- [Explainable Object-induced Action Decision for Autonomous Vehicles](https://mlsanity.com/paper/2003.09405/) (2020)
- [Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning](https://mlsanity.com/paper/2309.06597/) (2023)
- [Predicting Driver Attention in Critical Situations](https://mlsanity.com/paper/1711.06406/) (2017)
- [Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions](https://mlsanity.com/paper/2009.06435/) (2020)
- [Interaction Graphs for Object Importance Estimation in On-road Driving Videos](https://mlsanity.com/paper/2003.06045/) (2020)
- [Important Object Identification with Semi-Supervised Learning for Autonomous Driving](https://mlsanity.com/paper/2203.02634/) (2022)
- [Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles](https://mlsanity.com/paper/2011.13588/) (2020)
- [End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023](https://mlsanity.com/paper/2401.08658/) (2023)
- [Off The Beaten Sidewalk: Pedestrian Prediction In Shared Spaces For Autonomous Vehicles](https://mlsanity.com/paper/2006.00962/) (2020)
- [An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos](https://mlsanity.com/paper/2209.07922/) (2022)
- [Learning Pedestrian Actions to Ensure Safe Autonomous Driving](https://mlsanity.com/paper/2305.13051/) (2023)
- [Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera](https://mlsanity.com/paper/2005.01632/) (2020)
- [Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image](https://mlsanity.com/paper/2103.07570/) (2021)
- [Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis](https://mlsanity.com/paper/2403.09571/) (2024)
- [Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image](https://mlsanity.com/paper/2210.13646/) (2022)

## Abstract (arXiv)

This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either detecting objects independently or leveraging visual relationships, but they do not explicitly consider the ego vehicle's perspective in determining object importance. To address this gap, we propose a structured approach that integrates a virtual ego-vehicle representation and a modular object state predictor, enabling a more accurate estimation of object behaviors relative to the ego-vehicle. Subsequently, our framework employs spatial-temporal reasoning to refine key object identification, prioritizing objects based on their states and relative spatial information rather than relying solely on visual relationships. Experimental results on real-world driving datasets demonstrate the effectiveness of our approach in accurately detecting critical objects in complex traffic environments.