# End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera

arXiv: https://arxiv.org/abs/2006.04082 · HTML: https://mlsanity.com/paper/2006.04082/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image](https://mlsanity.com/paper/2204.01586/) (2022)
- [R4D: Utilizing Reference Objects for Long-Range Distance Estimation](https://mlsanity.com/paper/2206.04831/) (2022)
- [Multi-Stream Attention Learning for Monocular Vehicle Velocity and Inter-Vehicle Distance Estimation](https://mlsanity.com/paper/2110.11608/) (2021)
- [Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography](https://mlsanity.com/paper/2604.12239/) (2026)

## Abstract (arXiv)

Inter-vehicle distance and relative velocity estimations are two basic functions for any ADAS (Advanced driver-assistance systems). In this paper, we propose a monocular camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames, which include deep feature clue, scene geometry clue, as well as temporal optical flow clue. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field (i.e. optical flow). We implement the method by a light-weight deep neural network. Extensive experiments are conducted which confirm the superior performance of our method over other state-of-the-art methods, in terms of estimation accuracy, computational speed, and memory footprint.