Provident vehicle detection at night for advanced driver assistance systems

In recent years, computer vision algorithms have become more powerful, which enabled technologies such as autonomous driving to evolve rapidly. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently (denoted as provident vehicle detection). For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively (react before the oncoming vehicle is directly visible). Using this experimental setting, the provident vehicle detection system’s time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently (detection based on observable visual cues the objects cause before they are visible) and further close the performance gap between human behavior and computer vision algorithms to bring autonomous and automated driving a step forward

Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190{\deg}+ field of view covering the entire 360{\deg} around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation System

Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera Geometry

A fundamental challenge in robot perception is the coupling of the sensor pose and robot pose. This has led to research in active vision where robot pose is changed to reorient the sensor to areas of interest for perception. Further, egomotion such as jitter, and external effects such as wind and others affect perception requiring additional effort in software such as image stabilization. This effect is particularly pronounced in micro-air vehicles and micro-robots who typically are lighter and subject to larger jitter but do not have the computational capability to perform stabilization in real-time. We present a novel microelectromechanical (MEMS) mirror LiDAR system to change the field of view of the LiDAR independent of the robot motion. Our design has the potential for use on small, low-power systems where the expensive components of the LiDAR can be placed external to the small robot. We show the utility of our approach in simulation and on prototype hardware mounted on a UAV. We believe that this LiDAR and its compact movable scanning design provide mechanisms to decouple robot and sensor geometry allowing us to simplify robot perception. We also demonstrate examples of motion compensation using IMU and external odometry feedback in hardware.Comment: This paper is published in IEEE Transactions on Robotic

Vision based environment perception system for next generation off-road ADAS : innovation report

Advanced Driver Assistance Systems (ADAS) aids the driver by providing information or automating the driving related tasks to improve driver comfort, reduce workload and improve safety. The vehicle senses its external environment using sensors, building a representation of the world used by the control systems. In on-road applications, the perception focuses on establishing the location of other road participants such as vehicles and pedestrians and identifying the road trajectory. Perception in the off-road environment is more complex, as the structure found in urban environments is absent. Off-road perception deals with the estimation of surface topography and surface type, which are the factors that will affect vehicle behaviour in unstructured environments. Off-road perception has seldom been explored in automotive context. For autonomous off-road driving, the perception solutions are primarily related to robotics and not directly applicable in the ADAS domain due to the different goals of unmanned autonomous systems, their complexity and the cost of employed sensors. Such applications consider only the impact of the terrain on the vehicle safety and progress but do not account for the driver comfort and assistance. This work addresses the problem of processing vision sensor data to extract the required information about the terrain. The main focus of this work is on the perception task with the constraints of automotive sensors and the requirements of the ADAS systems. By providing a semantic representation of the off-road environment including terrain attributes such as terrain type, description of the terrain topography and surface roughness, the perception system can cater for the requirements of the next generation of off-road ADAS proposed by Land Rover. Firstly, a novel and computationally efficient terrain recognition method was developed. The method facilitates recognition of low friction grass surfaces in real-time with high accuracy, by applying machine learning Support Vector Machine with illumination invariant normalised RGB colour descriptors. The proposed method was analysed and its performance was evaluated experimentally in off-road environments. Terrain recognition performance was evaluated on a variety of different surface types including grass, gravel and tarmac, showing high grass detection performance with accuracy of 97%. Secondly, a terrain geometry identification method was proposed which facilitates semantic representation of the terrain in terms of macro terrain features such as slopes, crest and ditches. The terrain geometry identification method processes 3D information reconstructed from stereo imagery and constructs a compact grid representation of the surface topography. This representation is further processed to extract object representation of slopes, ditches and crests. Thirdly, a novel method for surface roughness identification was proposed. The surface roughness descriptor is then further used to recommend a vehicle velocity, which will maintain passenger comfort. Surface roughness is described by the Power Spectral Density of the surface profile which correlates with the acceleration experienced by the vehicle. The surface roughness descriptor is then mapped onto vehicle speed recommendation so that the speed of the vehicle can be adapted in anticipation of the surface roughness. Terrain geometry and surface roughness identification performance were evaluated on a range of off-road courses with varying topology showing the capability of the system to correctly identify terrain features up to 20 m ahead of the vehicle and analyse surface roughness up to 15 m ahead of the vehicle. The speed was recommended correctly within +/- 5 kph. Further, the impact of the perception system on the speed adaptation was evaluated, showing the improvements in speed adaptation allowing for greater passenger comfort. The developed perception components facilitated the development of new off-road ADAS systems and were successfully applied in prototype vehicles. The proposed off-road ADAS are planned to be introduced in future generations of Land Rover products. The benefits of this research also included new Intellectual Property generated for Jaguar Land Rover. In the wider context, the enhanced off-road perception capability may facilitate further development of off-road automated driving and off-road autonomy within the constraints of the automotive platfor

頑健な画像間対応付け及び視覚的位置推定のための深層学習手法

Tohoku University博士（情報科学）thesi

Deep Structured Layers for Instance-Level Optimization in 2D and 3D Vision

The approach we present in this thesis is that of integrating optimization problems as layers in deep neural networks. Optimization-based modeling provides an additional set of tools enabling the design of powerful neural networks for a wide battery of computer vision tasks. This thesis shows formulations and experiments for vision tasks ranging from image reconstruction to 3D reconstruction. We first propose an unrolled optimization method with implicit regularization properties for reconstructing images from noisy camera readings. The method resembles an unrolled majorization minimization framework with convolutional neural networks acting as regularizers. We report state-of-the-art performance in image reconstruction on both noisy and noise-free evaluation setups across many datasets. We further focus on the task of monocular 3D reconstruction of articulated objects using video self-supervision. The proposed method uses a structured layer for accurate object deformation that controls a 3D surface by displacing a small number of learnable handles. While relying on a small set of training data per category for self-supervision, the method obtains state-of-the-art reconstruction accuracy with diverse shapes and viewpoints for multiple articulated objects. We finally address the shortcomings of the previous method that revolve around regressing the camera pose using multiple hypotheses. We propose a method that recovers a 3D shape from a 2D image by relying solely on 3D-2D correspondences regressed from a convolutional neural network. These correspondences are used in conjunction with an optimization problem to estimate per sample the camera pose and deformation. We quantitatively show the effectiveness of the proposed method on self-supervised 3D reconstruction on multiple categories without the need for multiple hypotheses

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology