# Delay-Compensated Lane-Coordinate Vehicle State Estimation Using Low-Cost Sensors

## TL;DR

This paper introduces a new vehicle state estimator that uses low-cost sensors and compensates for signal delays to improve accuracy in autonomous driving systems.

## Contribution

A novel delay-compensated vehicle state estimator combining a bicycle model, EKF, and signal delay compensation for lane-coordinate systems.

## Key findings

- The estimator achieves high-accuracy lateral position, velocity, and heading angle estimation in real time.
- Compared to camera-only methods, it significantly reduces estimation errors and phase lag.
- Validation on real driving data shows reliable performance on both straight and curved roads.

## Abstract

Accurate vehicle state estimation in a lane coordinate system is essential for safe and reliable operation of Advanced Driver Assistance Systems (ADASs) and autonomous driving. However, achieving robust lane-based state estimation using only low-cost sensors, such as a camera, an IMU, and a steering angle sensor, remains challenging due to the complexity of vehicle dynamics and the inherent signal delays in vision systems. This paper presents a lane-coordinate-based vehicle state estimator that addresses these challenges by combining a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF) and a signal delay compensation algorithm. The estimator performs real-time estimation of lateral position, lateral velocity, and heading angle, including the unmeasurable lateral velocity about the lane, by predicting the vehicle’s state evolution during camera processing delays. A computationally efficient camera processing pipeline, incorporating lane segmentation via a pre-trained network and lane-based state extraction, is implemented to support practical applications. Validation using real vehicle driving data on straight and curved roads demonstrates that the proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states. Compared to conventional camera-only methods and estimators without delay compensation, the proposed approach significantly reduces estimation errors and phase lag, enabling the reliable and real-time acquisition of vehicle-state information critical for ADAS and autonomous driving applications.