# Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios

arXiv: https://arxiv.org/abs/2405.05354 · HTML: https://mlsanity.com/paper/2405.05354/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers](https://mlsanity.com/paper/2106.05392/) (2021)
- [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [Long-Tailed Recognition via Weight Balancing](https://mlsanity.com/paper/2203.14197/) (2022)
- [Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning](https://mlsanity.com/paper/1811.02307/) (2018)
- [TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions](https://mlsanity.com/paper/1812.04767/) (2018)
- [Temporal Recurrent Networks for Online Action Detection](https://mlsanity.com/paper/1811.07391/) (2018)
- [VideoLT: Large-scale Long-tailed Video Recognition](https://mlsanity.com/paper/2105.02668/) (2021)
- [Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs](https://mlsanity.com/paper/1912.01118/) (2019)
- [Invariant Feature Learning for Generalized Long-Tailed Classification](https://mlsanity.com/paper/2207.09504/) (2022)
- [Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks](https://mlsanity.com/paper/1909.09272/) (2019)
- [Use Your Head: Improving Long-Tail Video Recognition](https://mlsanity.com/paper/2304.01143/) (2023)
- [CMetric: A Driving Behavior Measure Using Centrality Functions](https://mlsanity.com/paper/2003.04424/) (2020)
- [RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs](https://mlsanity.com/paper/1907.08752/) (2019)
- [B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving](https://mlsanity.com/paper/2011.03748/) (2020)
- [METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors](https://mlsanity.com/paper/2109.07648/) (2021)
- [Using Graph-Theoretic Machine Learning to Predict Human Driver Behavior](https://mlsanity.com/paper/2111.02964/) (2021)
- [GraphRQI: Classifying Driver Behaviors Using Graph Spectrums](https://mlsanity.com/paper/1910.00049/) (2019)
- [Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning](https://mlsanity.com/paper/2303.00977/) (2023)
- [DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features](https://mlsanity.com/paper/1906.10313/) (2019)
- [RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments](https://mlsanity.com/paper/1906.10712/) (2019)
- [Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition](https://mlsanity.com/paper/2211.13471/) (2022)

## Built on by (most cited first, 1 of 1)

- [DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments](https://mlsanity.com/paper/2412.20042/) (2024)

## Abstract (arXiv)

Recognizing driving behaviors is important for downstream tasks such as reasoning, planning, and navigation. Existing video recognition approaches work well for common behaviors (e.g. "drive straight", "brake", "turn left/right"). However, the performance is sub-par for underrepresented/rare behaviors typically found in tail of the behavior class distribution. To address this shortcoming, we propose Transfer-LMR, a modular training routine for improving the recognition performance across all driving behavior classes. We extensively evaluate our approach on METEOR and HDD datasets that contain rich yet heavy-tailed distribution of driving behaviors and span diverse traffic scenarios. The experimental results demonstrate the efficacy of our approach, especially for recognizing underrepresented/rare driving behaviors.