A Comparison of Road Grade Preview Signals from Lidar and Maps

TL;DR

This paper compares lidar-based and map-based methods for estimating road grade to improve automated vehicle control, demonstrating lidar's viability for real-time, accurate grade measurement.

Contribution

It introduces a lidar-based road grade estimation method using point cloud data and Kalman filtering, showing comparable accuracy to map-based systems.

Findings

Lidar-based estimator has a standard deviation of 0.6 degrees at 52.7 meters.

Lidar method provides unbiased, real-time road grade estimates.

Lidar-based approach enhances vehicle control robustness and independence.

Abstract

Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering techniques are used to mitigate the effects of odometry and motion uncertainty on the grade estimates. This estimator's performance is compared to the measurements of a map created with a GNSS/INS system via a field experiment. When compared to the map-based system, the lidar-based estimator produces an unbiased error with a standard deviation of 0.6 degrees at an average range of 52.7 meters. By having similar precision to map-based systems, automotive lidar-based grade estimation systems are shown to be a valid approach for measuring road grade when a map is unavailable or inaccurate. In using lidar as an input signal for grade-based control system tasks, autonomous vehicles achieve higher redundancy and independence in contrast to existing methods.