# Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles

arXiv: https://arxiv.org/abs/2008.10869 · HTML: https://mlsanity.com/paper/2008.10869/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Video action recognition for lane-change classification and prediction of surrounding vehicles](https://mlsanity.com/paper/2101.05043/) (2021)
- [Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks](https://mlsanity.com/paper/2109.10742/) (2021)
- [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://mlsanity.com/paper/2208.09588/) (2022)
- [Lane Change Classification and Prediction with Action Recognition Networks](https://mlsanity.com/paper/2208.11650/) (2022)
- [Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction](https://mlsanity.com/paper/2509.20218/) (2025)
- [Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks](https://mlsanity.com/paper/2203.10707/) (2022)
- [Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems](https://mlsanity.com/paper/2401.03587/) (2024)
- [From Correlation to Causation in Lane Change Prediction for Automated Driving: A Causal Explanation Framework](https://mlsanity.com/paper/2606.15756/) (2026)

## Abstract (arXiv)

In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An automated system must anticipate these situations at an early stage too, to increase the safety and the efficiency of its performance. To deal with lane-change recognition and prediction of surrounding vehicles, we pose the problem as an action recognition/prediction problem by stacking visual cues from video cameras. Two video action recognition approaches are analyzed: two-stream convolutional networks and spatiotemporal multiplier networks. Different sizes of the regions around the vehicles are analyzed, evaluating the importance of the interaction between vehicles and the context information in the performance. In addition, different prediction horizons are evaluated. The obtained results demonstrate the potential of these methodologies to serve as robust predictors of future lane-changes of surrounding vehicles in time horizons between 1 and 2 seconds.