# Lane Change Classification and Prediction with Action Recognition Networks

arXiv: https://arxiv.org/abs/2208.11650 · HTML: https://mlsanity.com/paper/2208.11650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://mlsanity.com/paper/1805.05499/) (2018)
- [ConvNet Architecture Search for Spatiotemporal Feature Learning](https://mlsanity.com/paper/1708.05038/) (2017)
- [Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles](https://mlsanity.com/paper/2008.10869/) (2020)

## Built on by (most cited first, 3 of 3)

- [SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset](https://mlsanity.com/paper/2401.01425/) (2024)
- [Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction](https://mlsanity.com/paper/2509.20218/) (2025)
- [From Correlation to Causation in Lane Change Prediction for Automated Driving: A Causal Explanation Framework](https://mlsanity.com/paper/2606.15756/) (2026)

## Abstract (arXiv)

Anticipating lane change intentions of surrounding vehicles is crucial for efficient and safe driving decision making in an autonomous driving system. Previous works often adopt physical variables such as driving speed, acceleration and so forth for lane change classification. However, physical variables do not contain semantic information. Although 3D CNNs have been developing rapidly, the number of methods utilising action recognition models and appearance feature for lane change recognition is low, and they all require additional information to pre-process data. In this work, we propose an end-to-end framework including two action recognition methods for lane change recognition, using video data collected by cameras. Our method achieves the best lane change classification results using only the RGB video data of the PREVENTION dataset. Class activation maps demonstrate that action recognition models can efficiently extract lane change motions. A method to better extract motion clues is also proposed in this paper.