Takayuki Miyahara

Recognition of foggy conditions by in-vehicle camera and millimeter wave radar

Recently driving support techniques using in-vehicle sensors have attracted much attention and have been applied to practical systems. We focus on supporting drivers in poor visibility conditions. Fog is one of the causes that lead to lack of visibility. In this paper, we propose a method of judging fog density using in-vehicle camera images and millimeter-wave (mm-W) radar data. This method determines fog density by evaluating both the visibility of a preceding vehicle and distance to it. Experiments showed that judgments made by the proposed method achieved a recognition rate of 84% when compared to the ground truth obtained by human judgments.

Visibility Estimation in Foggy Conditions by In-Vehicle Camera and Radar

We propose a method of judging fog density by using in-vehicle camera images and millimeter-wave (mm-W) radar data. This method determines fog density by evaluating both the visibility of a preceding vehicle and the distance to it. Experiments revealed that judgments made by the proposed method achieved an 85% precision rate compared to that mode by human subjects

Measurement of Visibility Conditions toward Smart Driver Assistance for Traffic Signals

We propose a method to recognize the visibility of traffic signals from a drivers perspective. The more that driver assistance systems are equipped for practical use, the more information that is being provided for drivers. So each information provision system should select appropriate information based on the situation. Our goal is to realize a system that recognizes the visibility of traffic signals from images taken by in-vehicle cameras and appropriately provides information to drivers. In this paper, we propose a method to measure visibility by two criterions, detectability and discriminability. Each index is computed using image processing techniques. Experiments using actual images showed that the proposed indices correspond well to human perception.

Raindrop Detection from In-Vehicle Video Camera Images for Rainfall Judgment

In this paper, we propose a method to detect raindrops from in-vehicle camera images and recognize rainfall using time-series information. We aim to improve the accuracy of raindrop detection by averaging the test images and frame-matching the result of raindrop detection in multiple adjoining frames. According to an evaluation experiment, raindrops were detected precisely enough for automatic wiper control by the proposed method

Traffic interactions: Estimate driving behavior's influence

In this paper, we propose a novel approach to deal influence of own vehicle behavior to driving scene which called “interactions” with surrounding traffic participants. Recently, various advanced driver-assistance systems (ADAS) have been proposed. In these ADAS, however, it is not sufficiently considering the influence of own vehicle behavior. With a novel driver assistance system based on the traffic interactions, each vehicle keeps not only own vehicle but also surrounding space in safety and comfortable, such as, Lane Change Assist for reducing traffic jam. We estimate the interactions from the behavior data of the traffic participants using Bayesian filtering techniques. Efficiency of the novel driving support with the interactions is evaluated in simple traffic simulations. In the simulated experiments, our approach improves traffic flow 140% smoother than without the driving support. Constructions of more detail traffic interaction models and demonstrations of effectiveness using real-vehicles are important feature works. It is also important that the development of the specific ADAS application based on traffic interaction.

Abnormal Driving Behavior Detection Using a Linear Non-Gaussian Acyclic Model for Causal Discovery

This paper proposed a causal discovery method for driving behavior signals representing explicit driving actions to gain a better understanding in driving actions and to apply them to improve advanced driver assistance systems. The first- and second-order statistics have only correlation information and hence they cannot tell anything on causality. In other words, causality appears in higher-order statistics. To utilize such information, we apply a linear non-Gaussian acyclic model (LiNGAM) that assumes non-Gaussian observation noise to the driving actions. Non-Gaussian assumption makes it possible to use ICA to estimate the mixing matrix of observed variables. The mixing matrix can transform to a triangular matrix that expresses causality. This method is called ICA-LiNGAM. ICA-LiNGAM can extract causality in time-series of vectors. Driving actions are time-series of vectors that play an essential role in the safe driving. To obtain driving data, we carried out experiments in which subjects drove a freeway without any instruction except for its route. In our method, both instantaneous and lagged causal influences have been considered by using the Structural Vector Autoregression (SVAR) to the continuous time series data. SVAR model is a generalization of LiNGAM model so the assumption of independence and non-Gaussianity is ensured for ICA-LiNGAM analysis. The conventional LiNGAM can be regarded as a special case of SVAR when the autoregressive order is zero. In the experiment ICA-LiNGAM worked well with SVAR model and the analysis result showed a clear causal relationship between lagged variables of driving action signals. The experiment shows that ICA-LiNGAM can find out both the obvious and latent causal relations between driving behaviors.