Kosuke Hara

Vehicle localization based on the detection of line segments from multi-camera images

For realizing autonomous vehicle driving and advanced safety systems, it is necessary to achieve accurate vehicle localization in cities. This paper proposes a method of accurately estimating vehicle position by matching a map and line segment features detected from images captured by a camera. Features such as white road lines, yellow road lines, road signs, and curb stones, which could be used as clues for vehicle localization, were expressed as line segment features on a two-dimensional road plane in an integrated manner. The detected line segments were subjected to bird’s-eye view transformation to transform them to the vehicle coordinate system so that they could be used for vehicle localization regardless of the camera configuration. Moreover, an extended Kalman filter was applied after a detailed study of the line observation errors for realizing real-time estimation. Vehicle localization was tested under city driving conditions, and the vehicle position was identified with sub-meter accuracy.

Danger level modeling and analysis of vehicle-pedestrian encounter using situation dependent topic model

The mechanism behind collisions between vehicles and pedestrians must be thoroughly studied in order to prevent future traffic accidents. In particular, preventing collisions where pedestrian steps out onto the road from behind an obstruction such as buildings, walls or vehicles is a challenging problem. To tackle this problem, we propose situation dependent topic model (SDTM), a regression model that predicts dangerous vehicle-pedestrian encounter in response to different driving situations, which also provides a framework to analyze and understand the underlying factors that lead to dangerous situations. Complex nature of situations where collisions with pedestrians happen can be expressed well by defining how dangerous situations arise differently for each driving situation pattern retrieved using statistical topic modeling. In experiments, we compare the performance of SDTM with orthodox logistic regression models using vehicle-pedestrian encounters in near-miss incidents. We also show the result of acquired knowledge that can form the basis of many other researches concerning pedestrian safety.

Line-based SLAM considering directional distribution of line features in an urban environment

In this paper, we propose a line-based SLAM from an image sequ ence captured by a vehicle in consideration with the directional distribution of line features that det ected in an urban environments. The proposed SLAM is based on line segments detected from objects in an urban en vironment, for example, road markings and buildings, that are too conspicuous to be detected. We use ad ditional constraints regarding the line segments so that we can improve the accuracy of the SLAM. We assume that the ngle of the vector of the line segments to the vehicle’s direction of travel conform to four-compon ent Gaussian mixture distribution. We define a new cost function considering the distribution and optimiz e the relative camera pose, position, and the 3D line segments by bundle adjustment. In addition, we make dig ital maps from the detected line segments. Our method increases the accuracy of localization and revises t ilt d lines in the digital maps. We implement our method to both the single-camera system and the multi-camer a system. The accuracy of SLAM, which uses a single-camera system with our constraint, works just as we ll as a method that uses a multi-camera system without our constraint.

Motion estimation for non-overlapping cameras by improvement of feature points matching based on urban 3D structure

We propose a method of ego-motion estimation for a self-driving vehicle using multiple cameras. By finding corresponding points between the multi-camera images, we aim to enhance the accuracy of the ego-motion estimation. However since the viewing directions are very different from one camera to the other, a conventional algorithm such as SURF cannot detect a sufficient number of correspondences. We propose a novel matching algorithm by warping feature patches detected in different cameras based on urban 3D structure. We assume that detected features exist on the surface of buildings or roads and the patch around the feature is planar. Based on this assumption, we can warp the patches so that the feature descriptors are similar for the corresponding feature points. We apply Bundle Adjustment to the found correspondences to optimizes the odometry. The result shows higher estimation accuracy when compared to other matching method.