# Anticipating Driving Behavior through Deep Learning-Based Policy Prediction

arXiv: https://arxiv.org/abs/2307.11058 · HTML: https://mlsanity.com/paper/2307.11058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [Visual News: Benchmark and Challenges in News Image Captioning](https://mlsanity.com/paper/2010.03743/) (2020)
- [Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps](https://mlsanity.com/paper/2307.05052/) (2023)
- [COVID-VTS: Fact Extraction and Verification on Short Video Platforms](https://mlsanity.com/paper/2302.07919/) (2023)
- [DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents](https://mlsanity.com/paper/2306.06306/) (2023)

## Abstract (arXiv)

In this endeavor, we developed a comprehensive system that processes integrated visual features derived from video frames captured by a regular camera, along with depth details obtained from a point cloud scanner. This system is designed to anticipate driving actions, encompassing both vehicle speed and steering angle. To ensure its reliability, we conducted assessments where we juxtaposed the projected outcomes with the established norms adhered to by skilled real-world drivers. Our evaluation outcomes indicate that the forecasts achieve a noteworthy level of accuracy in a minimum of half the test scenarios (ranging around 50-80%, contingent on the specific model). Notably, the utilization of amalgamated features yielded superior performance in comparison to using video frames in isolation, as demonstrated by most of the cases.