# Optical Flow based Visual Potential Field for Autonomous Driving

arXiv: https://arxiv.org/abs/2006.00168 · HTML: https://mlsanity.com/paper/2006.00168/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 16 of 16)

- [Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2109.04871/) (2021)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow](https://mlsanity.com/paper/2203.13214/) (2022)
- [StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences](https://mlsanity.com/paper/2311.17099/) (2023)
- [SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving](https://mlsanity.com/paper/2303.06209/) (2023)
- [ADFactory: An Effective Framework for Generalizing Optical Flow with Nerf](https://mlsanity.com/paper/2311.04246/) (2023)
- [Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-world](https://mlsanity.com/paper/2410.10453/) (2024)
- [Towards Dependable Optical Flow: Benchmarks for Real-World Spatiotemporal Corruptions](https://mlsanity.com/paper/2411.14865/) (2024)
- [Investigation of Factorized Optical Flows as Mid-Level Representations](https://mlsanity.com/paper/2203.04927/) (2022)
- [Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering](https://mlsanity.com/paper/2409.12716/) (2024)
- [Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference](https://mlsanity.com/paper/2409.10095/) (2024)
- [Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation](https://mlsanity.com/paper/2312.07180/) (2023)
- [HMAFlow: Learning More Accurate Optical Flow via Hierarchical Motion Field Alignment](https://mlsanity.com/paper/2409.05531/) (2024)
- [FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion](https://mlsanity.com/paper/2607.10140/) (2026)
- [Physically Real-time Infrared Attack against Optical Flow Estimation Networks](https://mlsanity.com/paper/2607.26651/) (2026)
- [Optical Flow from Photons](https://mlsanity.com/paper/2608.00499/) (2026)

## Abstract (arXiv)

Monocular vision-based navigation for automated driving is a challenging task due to the lack of enough information to compute temporal relationships among objects on the road. Optical flow is an option to obtain temporal information from monocular camera images and has been used widely with the purpose of identifying objects and their relative motion. This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow, which is used together with a gradient tracking sliding mode controller to navigate the vehicle to destination without collision with obstacles. The angular reference for the vehicle is computed online. This work considers that the vehicle does not require to have a priori information from the map or obstacles to navigate successfully. The proposed technique is tested both in synthetic and real images.