Forward Lidar Road Profiling for Low-Delay Condition Estimation
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Solution Overview
Problem
Existing methods for estimating road conditions, such as using IMU, wheel speed sensors, and map service providers, face delays, offsets, and limited accuracy, especially in low visibility situations and for predicting road conditions ahead.
Innovation Solution
A vehicle system that uses a 3D sensor, such as a LIDAR sensor, to obtain data points representing longitudinal, lateral, and vertical coordinates of the road. The system segments these data points into areas based on longitudinal and lateral coordinates and estimates the vertical position in each area using motion state data from the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IMU and wheel speed sensors are used for road condition estimation, then road condition parameters can be obtained, but delays and offsets are introduced in the estimation
Solution Approach 1:
The patent replaces mechanical sensors (IMU, wheel speed sensors) with a LIDAR-based optical system for road condition estimation. The LIDAR sensor directly measures the vertical position of the road surface by emitting light pulses and detecting the reflected light, eliminating the need for mechanical motion sensors and their associated delays in estimating road geometry parameters.
2Loss of information
If map service providers are used for road condition information, then road condition data can be obtained, but availability and accuracy depend on map resolution and features
Solution Approach 1:
The system uses the vehicle's own LIDAR sensor to directly measure road surface vertical positions, making the vehicle self-sufficient for road condition estimation. This eliminates dependency on external map service providers and their resolution limitations, as the system generates its own road condition data through direct optical measurement of the road surface.
3Measurement precision
If camera images are used for road damage and anomaly detection, then visual information can be obtained, but accuracy is limited especially in low visibility situations
Solution Approach 1:
The patent replaces camera-based visual detection with LIDAR-based optical ranging. The LIDAR sensor actively emits light pulses and measures the time of flight of reflected light to determine vertical positions, providing active illumination that is not affected by ambient lighting conditions. This enables accurate road anomaly detection regardless of visibility conditions, unlike passive camera systems.
4Measurement precision
If suspension signals are used to estimate road condition, then current road position information can be obtained, but future road conditions ahead cannot be estimated
Solution Approach 1:
The LIDAR sensor is mounted to face forward and scan the road surface ahead of the vehicle, performing preliminary measurement of future road conditions before the vehicle reaches them. This allows the system to estimate vertical positions and detect anomalies in upcoming road sections, enabling predictive road condition information that suspension signals cannot provide.
5Loss of information
If image processing algorithms with deep learning are used to estimate future road conditions, then road condition prediction can be achieved, but the system becomes computationally expensive
Solution Approach 1:
The patent replaces complex deep learning image processing algorithms with a direct geometric calculation approach. The LIDAR provides precise vertical position measurements, and the system calculates road condition parameters through straightforward coordinate transformation and motion compensation using vehicle motion state data, dramatically reducing computational energy consumption while maintaining prediction capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method allows for accurate and efficient estimation of road conditions, including geometrical profiles and anomalies, enabling improved driving safety, ride comfort, and energy efficiency by adjusting vehicle parameters such as suspension, steering, and regenerative braking.
Implementation Method 1
one or more Light Detection and Ranging, LIDAR, sensors
Implementation Method 2
Lidar sensors transmit light pulses at certain known angles and receive the returned light after reflection, thereby being able to calculate the distance to objects
Data Source
AI summary
A method performed by a vehicle system for enabling estimation of a condition of a road comprising obtaining, from a sensor mounted on a vehicle, first data comprising a first plurality of data points, each comprising longitudinal, lateral and vertical coordinates representing dimensions of a part of the road at a first time, obtaining a second plurality of data points, each comprising longitudinal, lateral and vertical coordinates representing dimensions of the part of the road at a second time, wherein the first time is more recent than the second time, segmenting the first plurality of data points into a plurality of segmented data areas based on longitudinal and lateral coordinates of the first plurality of data points, and estimating a respective vertical position in each segmented data area based on vertical coordinates of the second plurality of data points and a motion state of the vehicle at the second time.