Terrain Profile Rut Detection for Self-Driving Path Adjustment
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Solution Overview
Problem
Self-driving cars face challenges in safely navigating terrains with road ruts, which can increase the risk of collisions and loss of control due to uneven surfaces.
Innovation Solution
A method and system for detecting road ruts using LIDAR sensors to generate terrain profiles, comparing them with stored profiles to identify rut presence, and adjusting the vehicle's reference path and trajectory to avoid these ruts, thereby enhancing safety and reducing collision risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the SDC follows the default reference path along the center-line of the road, then the navigation is simplified and operation is easier, but the risk of collision increases when ruts are present on the terrain
Solution Approach 1:
The system performs preliminary detection of ruts using LIDAR sensors and compares current terrain profiles with stored profiles before the vehicle reaches the hazardous area. This advance detection allows the navigation system to pre-calculate alternative paths that avoid ruts, thereby maintaining operational simplicity while eliminating collision risk.
Solution Approach 2:
The system continuously monitors terrain using LIDAR sensors and compares real-time terrain profiles with stored reference profiles. When ruts are detected through profile comparison, the system provides feedback to the navigation module to dynamically adjust the reference path, ensuring both operational ease and safety by adapting to detected terrain conditions.
2Reliability
If the SDC uses sensor data to detect terrain features, then the safety is improved, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The LIDAR sensor system is designed to serve multiple functions: it detects not only ruts but also other terrain features, obstacles, and navigation markers. The same sensor data and processing pipeline are used for both rut detection and general navigation, thereby improving safety without proportionally increasing system complexity.
Solution Approach 2:
The system stores reference terrain profiles in advance and creates simplified digital models of the terrain. Instead of processing raw LIDAR point clouds in real-time, the system compares simplified terrain profiles against stored reference profiles, reducing computational complexity while maintaining detection accuracy and safety.
3Reliability
If the SDC modifies the reference path to avoid detected ruts, then the collision risk is reduced, but the navigation complexity and processing time increase
Solution Approach 1:
The system pre-stores reference terrain profiles and pre-calculates alternative paths that avoid known rut locations. When ruts are detected during operation, the system simply compares current terrain with stored profiles and switches to pre-planned alternative paths, thereby achieving effective collision avoidance with minimal real-time processing delay.
Solution Approach 2:
The reference path is designed to be dynamic and adaptable. Instead of recalculating the entire path when ruts are detected, the system dynamically adjusts the navigation by selecting from pre-computed alternative paths based on current terrain conditions, reducing processing time while maintaining collision avoidance 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
Effectively detects and avoids road ruts, improving the safety and control of self-driving cars by reducing the risk of collisions and maintaining stable operation on uneven terrains.
Implementation Method 1
A Self-Driving Car (SDC) is equipped with a sensor and an electronic device. The sensor may be configured to generate sensor data indicative of at least a surface of the current terrain.
Data Source
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AI summary
Method and device for detecting presence of ruts are disclosed. The method includes using sensor data for generating a current terrain profile (i) representing a height variation of the surface of the current terrain along its width, and (ii) having a pair of current grooves potentially indicative of the presence of the ruts on the current terrain. The method also includes acquiring a sampled terrain profile (i) representing a height variation of a surface of a given terrain along its width, and (ii) having a pair of sampled grooves indicative of a presence of the ruts on the given terrain. The method includes using the current terrain profile and the sampled terrain profile for generating comparison data indicative of a similarity between the current terrain profile and the sampled terrain profile. The method includes using the comparison data for detecting the presence of the ruts on the current terrain.