# Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

arXiv: https://arxiv.org/abs/1809.07408 · HTML: https://mlsanity.com/paper/1809.07408/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 30 of 42)

- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Stepwise Goal-Driven Networks for Trajectory Prediction](https://mlsanity.com/paper/2103.14107/) (2021)
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021)
- [Panoptic Video Scene Graph Generation](https://mlsanity.com/paper/2311.17058/) (2023)
- [EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation](https://mlsanity.com/paper/2406.18070/) (2024)
- [Predicting the Future from First Person (Egocentric) Vision: A Survey](https://mlsanity.com/paper/2107.13411/) (2021)
- [PALM: Predicting Actions through Language Models](https://mlsanity.com/paper/2311.17944/) (2023)
- [PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning](https://mlsanity.com/paper/2210.07886/) (2022)
- [Action-based Contrastive Learning for Trajectory Prediction](https://mlsanity.com/paper/2207.08664/) (2022)
- [Coupling Intent and Action for Pedestrian Crossing Behavior Prediction](https://mlsanity.com/paper/2105.04133/) (2021)
- [CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions](https://mlsanity.com/paper/2407.17757/) (2024)
- [A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.10424/) (2023)
- [egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks](https://mlsanity.com/paper/2502.20879/) (2025)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting](https://mlsanity.com/paper/2102.05624/) (2021)
- [Indoor Future Person Localization from an Egocentric Wearable Camera](https://mlsanity.com/paper/2103.04019/) (2021)
- [Multi-Camera Trajectory Forecasting with Trajectory Tensors](https://mlsanity.com/paper/2108.04694/) (2021)
- [Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting](https://mlsanity.com/paper/2108.10543/) (2021)
- [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)
- [XVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving](https://mlsanity.com/paper/2308.08764/) (2023)
- [Dense Semantic Forecasting in Video by Joint Regression of Features and Feature Motion](https://mlsanity.com/paper/2101.10777/) (2021)
- [Joint Forecasting of Panoptic Segmentations with Difference Attention](https://mlsanity.com/paper/2204.07157/) (2022)
- [Sparse Pedestrian Character Learning for Trajectory Prediction](https://mlsanity.com/paper/2311.15512/) (2023)
- [Crash Report Data Analysis for Creating Scenario-Wise, Spatio-Temporal Attention Guidance to Support Computer Vision-based Perception of Fatal Crash Risks](https://mlsanity.com/paper/2109.02710/) (2021)
- [Vehicle Type Specific Waypoint Generation](https://mlsanity.com/paper/2208.04987/) (2022)
- [A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos](https://mlsanity.com/paper/2307.14575/) (2023)

## Abstract (arXiv)

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic.