Monocular Depth Point Cloud Analysis for In-Lane Driving Behavior
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Solution Overview
Problem
Existing systems for analyzing in-lane driving behavior of external road agents in vehicles face high processing demands and increased costs due to the use of multi-modal sensors like LIDAR, necessitating a more efficient approach.
Innovation Solution
A system utilizing sequential monocular depth estimation and deep neural networks to generate sparse and dense 3D point clouds from camera images, combined with flow clustering and lane marking detection, to analyze the driving behavior of external road agents without the need for LIDAR sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors and multi-modal sensor fusion are used to analyze in-lane driving behavior, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and removes the LIDAR sensor from the sensor fusion system, relying solely on monocular camera images for depth estimation. This eliminates the need for complex multi-modal sensor integration while maintaining the capability to analyze in-lane driving behavior through computational methods.
Solution Approach 2:
The patent creates a virtual 3D representation (point cloud) from 2D camera images through monocular depth estimation. This virtual copy of the physical scene enables precise behavioral analysis without requiring physical LIDAR sensors to capture actual 3D data.
2Measurement precision
If LIDAR sensors and sensor fusion are deployed, then measurement precision is improved, but processing overhead increases
Solution Approach 1:
The patent removes the computationally intensive LIDAR processing pipeline and multi-modal sensor fusion algorithms, replacing them with monocular depth estimation from single camera images. This extraction of unnecessary processing steps significantly reduces computational overhead while maintaining analysis precision.
Solution Approach 2:
The patent changes the input data parameter from multi-modal sensor data (point clouds, depth maps from LIDAR) to single-modality 2D images with estimated depth. This parameter change simplifies the processing pipeline by eliminating the need to fuse and align multiple data types, improving processing efficiency.
3Measurement precision
If LIDAR sensors are used, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent replaces expensive LIDAR sensors with inexpensive monocular cameras. The camera is a low-cost, widely available component that can be integrated into vehicles without significantly increasing system cost, while still enabling precise depth estimation through computational methods.
Solution Approach 2:
The patent uses a virtual 3D model generated from 2D images as a substitute for physical LIDAR measurements. This virtual copy approach eliminates the need for expensive sensing hardware while achieving the same analytical objectives through software-based depth reconstruction.
Data Source
AI summary
One embodiment of a system for analyzing the in-lane driving behavior of an external road agent generates a sequence of sparse 3D point clouds based on a sequence of depth maps corresponding to a sequence of images of a scene. The system performs flow clustering based on the sequence of depth maps and a sequence of flow maps to identify points across the sequence of sparse 3D point clouds that belong to a detected road agent. The system generates a dense 3D point cloud by combining at least some of those identified points. The system detects one or more lane markings and projects them into the dense 3D point cloud to generate an annotated 3D point cloud. The system analyzes the in-lane driving behavior of the detected road agent based, at least in part, on the annotated 3D point cloud.