Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles

TL;DR

This paper introduces a new method for estimating inter-vehicle distances that accounts for road slopes by estimating gradients of both vehicles, improving accuracy and efficiency over existing methods that assume flat roads.

Contribution

The paper presents a novel framework that considers road gradients of both ego and target vehicles, enhancing distance estimation in varied terrains.

Findings

Significantly improves distance estimation accuracy.

Reduces computational complexity.

Effective in practical driving environments with slopes.

Abstract

In advanced driver assistant systems and autonomous driving, it is crucial to estimate distances between an ego vehicle and target vehicles. Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane. In practical driving environments, however, they may drive on different ground planes. This paper proposes an inter-vehicle distance estimation framework that can consider slope changes of a road forward, by estimating road gradients of \emph{both} ego vehicle and target vehicles and using a 2D object detection deep net. Numerical experiments demonstrate that the proposed method significantly improves the distance estimation accuracy and time complexity, compared to deep learning-based depth estimation methods.