Keiichi Uchimura

Demand responsive services in hierarchical public transportation system

An advanced public transportation system called the local initiative for neighborhood circulation (LINC) is proposed and the realization of its usage is investigated. LINC effectiveness and efficiency comes from its utilization of a transit-station-oriented system with elimination of present bus stops. LINC has three levels of services. Level 1 is the regional trunk line service with high-speed and high-capacity service provided by the regional transit authority. Level 2 is the intercommunity express bus service with frequent and reliable service provided by the metro transit system. Level 3 could have many forms to serve within the community and to/from level 1 or level 2 transit stations. Our study is limited to the dial-a-ride service, which operates door-to-door service as a public taxi, provided by small buses and/or vans. This paper will address the optimization problem of the dial-a-ride service. A genetic algorithm scheme is applied to its optimization and proper solutions are obtained as a result.

Real-time data fusion on tracking camera pose for direct visual guidance

To properly align objects in the real and virtual world in an augmented reality (AR) space, it is essential to keep tracking cameras exact 3D position and orientation, which is well known as the Registration problem. Traditional vision based or inertial sensor based solutions are mostly designed for well-structured environment, which is, however, unavailable for outdoor uncontrolled road navigation applications. This paper proposed a hybrid camera pose tracking system that combines vision, GPS and 3D inertial gyroscope technologies. The fusion approach is based on our PMM (parameterized model matching) algorithm, in which the road shape model is derived from the digital map referring to GPS absolute road position, and matches with road features extracted from the real image. Inertial data estimates the initial possible motion, and also serves as the relative tolerance to stabilize output. The algorithms proposed in this paper are validated with the experimental results of real road tests under different conditions and types of road.

Driver inattention monitoring system for intelligent vehicles: A review

This paper gives a review of the literature on driver inattention monitoring system for the purpose of active safe driving. In this paper driving inattention is classified into two categories: fatigue and distraction, while fatigue and distraction can also contain many types and levels. Individual difference on inattention phenomenon makes it more complicated to correctly detect and recognize driving inattention. Driver attention monitoring has been intensively researched in recent years and many approaches have been proposed, which include biological signal (EEG, ECG, EOG and sEMG) processing method, subjective report method, and behavior analysis method. This survey reviews a number of promising approaches and provides an overview of recent developments in this domain. The emphasis of this paper is to discuss the various methodologies to monitor driving inattention. We conclude with some thoughts about future directions.

Car detection based on multi-cues integration

We present a novel fast multi-cues based car detection technique in still outdoor images. On the bottom level, two novel area templates based on edge cue and interest points cue are first designed, which can rapidly reject most of the non-car sub-windows at the cost of missing few of the car sub-windows. On the top level, both global structure cue and local texture cue are considered. To character the global structure property the odd Gabor moments are introduced and trained by SVMs. The multi channels even Gabor based local texture property extracted from corner area is modeled as a Gaussian distribution. The final experiment results show that the integration of global structure property and local texture property is more powerful in discrimination between car and non-car objects and a high detection accurate 93% is obtained.

Tracking cycle: a new concept for simultaneous tracking of multiple moving objects in a typical traffic scene

Because of the complicated motion pattern of moving objects in a typical on-road traffic scene, detecting the real moving objects and properly tracking each one have become very difficult and always employ optical flows and fixed template matching to detect and track the moving objects. Unfortunately, it has to pay a huge calculation cost and can not deal with some complicated motion pattern like overlapping of tracked objects. A new concept of tracking cycle is proposed in the paper. With the continuous estimation of moving objects vitality and reliability values, our method can detect and track up to more than 30 moving objects in real time. The flexible dynamic template matching method is also presented in the paper, which makes our system faster and more efficient for real-time ITS applications. Experiments on real outdoor road scenes show the accuracy and efficiency of our approach.

3D Face Recognition Using Multi-level Multi-feature Fusion

This paper proposed a novel 3D face recognition algorithm using multi-level multi-feature fusions. A new face representation method named average edge image is proposed in addition to traditional ones such as maximal principal curvature image and range image. In the matching process stage, a new weight calculation algorithm based on the sum rule is presented for feature fusion and match score fusion in order to improve the matching precision. Depending on the complementary characteristic of feature fusion and match score fusion, a combination of them named two-level fusion is proposed. Experiments are conducted using our own 3D database consisting of nearly 400 samples. Mesh simplification is utilized for data reduction. Recognition results show that the new weight calculation method improves the recognition accuracy and the two-level fusion algorithm performs better than feature fusion and match score fusion.

Precise curvature estimation by cooperating with digital road map

This paper proposed a novel algorithm for road geometry estimation ahead of vehicle depending on present vehicle localization. In order to provide precise and robust on-coming road geometry estimation under different environmental conditions, we employ a new hybrid vehicle localization approach combining the commercial GPS and inertial sensors. In this work, road geometry (planar curvature) is initially estimated from the prevalent digital road map following the rules of road construction design and basic geometrical elements (straight line, clothoid curve and circle curve). Therefore, road trajectory between vehicle and road ahead could be calculated by flexible road model, relying on precise curvature which has been designed beforehand. In simulation test, road geometrical curve within camerapsilas view range is built by variance curvature in meters. Meanwhile, road tests show similarity between simulation results and real road scenes. This approach is also proved effectiveness and robustness under different kinds of environment conditions.

Vehicle Localization with Global Probability Density Function for Road Navigation

This paper presents a novel approach of real-time vehicles localization (position and orientation) estimation. Fusion of GPS, gyroscope, speedometer and visual data is employed here to provide real time and accurate localization information. Global probability density function(PDF) is adopted to be the blending factor instead of general Kalman gain, which allows our approach to be robust and accurate for most of practical systematic problems, since the basic measurements from GPS may cause data drift or large infrequent data jumps during the fusion processing. Combining with visual data for lane shape recognition and tracking, our approach can provide as accurate as 3 to 5 meters RMS location accuracy at about 30 Hz, with less then 35 ms delay. This approach has been adapted to the direct visual navigation system in VICNAS.

SLAM Estimation in Dynamic Outdoor Environments: A Review

This paper gives a review of the literature on Simultaneous Localization and Mapping (SLAM). SLAM has been intensively researched in recent years in the field of robotics and intelligent vehicles, many approaches have been proposed including occupancy grid mapping method (Bayesian, Dempster-Shafer and Fuzzy Logic), Localization estimation method (edge or point features based direct scan matching techniques, probabilistic likelihood, particle filter). In this paper, we classify SLAM approaches into three main categories: visual SLAM, Lidar SLAM and sensor fusion SLAM, while visual and lidar can also contain many types and levels, such as monocular camera, stereovision, laser scanner, radar and fusion of these sensors. A number of promising approaches and recent developments in this literature have been reviewed in this paper. To give a better understanding of performance difference, an implementation of Lidar SLAM is presented with comparative analysis result.

Vehicle routing problem using genetic algorithms based on adjacency relations

In the vehicle routing problem, the traditional crossover operators for genetic algorithms (GAs) could fail to produce legal tours. We propose a new crossover operator considering adjacency relations, and compare the new GA with the GA using the partially matched crossover or a branch-and-bound method. Some experiments are performed on digital road maps. The proposed GA finds optimum solutions effectively.