A novel trajectory generator for obstacle avoidance is proposed and evaluated through numerical simulations with common iterative methods. Since the proposed method generates quasi-optimal trajectories using model predictive control (MPC) theory with a predetermined upper bound on computational cost, it makes it easy to guarantee real-time feasibility for autonomous driving and/or driving support systems. Numerical round-robin simulations are conducted for both the proposed method and a comparative method, after which we evaluate the results through statistical analysis and individually analyze several characteristic results. Taken together, the results show that the proposed method generates trajectories that are statistically equivalent to those generated by the comparative method, while guaranteeing that the upper bound of the computational cost is predetermined.
In this study, authers theoretically discussed the difference in the function between two types of planetary gear inerters by comparing two fundamental functions of planetary gear inerter, one is “ideal inerter function” and another is “moment of inertia redistributing function.” According to the formulation of each planetary gear inerter, different characteristics were confirmed in the ideal inerter function and the moment of inertia redistributing function between two types of planetary gear inerters. Numerical simuration results demonstrated the importance of selection of the proper configuration for a planetary gear inerter in order to tune the frequency characteristics of the torsional vibration system. In addition, practical design issues of the planetary gear inerter could be successfully solved by introducing another type planetary gear inerter.
This paper describes a human factor considered risk assessment of automated vehicle using vehicle to vehicle wireless communication (V2V communication). A human-reaction time is incorporated into the probabilistic threat assessment algorithm for the human-centered risk assessment. The V2V communication have been fused with a radar sensor to achieve more enhanced tracking performance of automated vehicle. Information fusion of two tracks, V2V communication and radar sensor, is performed using Global Nearest Neighborhood (GNN) approach. A prediction of vehicle’s motion follows the basic idea of the particle filtering. Based on the predicted behavior of vehicles, a collision risk is computed numerically and 321 driver data based human reaction time are incorporated to determine an active safety control intervention moment. The humancentered risk assessment algorithm has been applied to a collision avoidance scenario to monitor threat vehicles ahead and to find the best intervention point. Effects of the vehicular communication on a target vehicle state estimation and a vehicle safety control performance are investigated. The performance of the proposed algorithm has been investigated via computer simulation studies. It has been shown from both simulations and vehicle tests that the proposed human-centered risk assessment algorithm with the V2V communication can be beneficial to active safety systems in decision of controller intervention moment and in control of automated drive for the guaranteed safety.
It is believed that Dynamic Mode Decomposition (DMD) is a very useful method for the analysis of unsteady aerodynamics of road vehicles. However, it seems that the conventional DMD method is not practical regarding the application on the aerodynamic design of road vehicles, since DMD computation requires massive memory. Alternatively, online DMD methods seem to be useful in practice, as those require much less memory than the conventional method. In this paper, further validation of the online DMD on the aerodynamic forces on the DrivAer model is conducted, through the comparison with results from other enhanced DMD methods and FFT.
Temporally resolved flow fields are commonly averaged in time, and mostly the time-averaged flow fields and forces are used for the aerodynamic optimization of road vehicles. Online DMD is found to be well suited for studying transient flow effects and leads to a deeper understanding of the complex flow around the vehicle. The investigated velocity field is computed by a Detached Eddy Simulation of the DrivAer reference body. The CFD setup and key considerations for the application of online DMD on large data sets are outlined, and the most dominant extracted coherent flow structures are analyzed independently.
Accurate self-localization is a critical problem in the autonomous driving system. In this paper, we proposed a localization system which integrates stereo camera and three-dimensional city model containing building and road mark information. The stereo camera generates visual odometry, reconstructs building scene and detects road mark. We aligned building scene with Normal Distribution Transform (NDT) map to get the absolute localization. Road mark detection result helps to rectify the inner lane positioning with road map. The experiment conducted in Hitotsubashi, Tokyo indicates that the lateral and heading error of visual odometry can be corrected and sub-meter accuracy localization is achieved.
Driving comfort and driving safety are essential factors for drivers. As the environment of vehicle affects the comfort sensation and the arousal level of the drivers, it is necessary to contemplate the way to design of environmental factors inside vehicle. In this study, we focus on the thermal factors inside vehicle, and we aim to design a thermal environment which can improve both the thermal comfort and the arousal level of drivers. In our previous research, we showed that the changes in indoor temperature have possibility to improve both the comfort sensation and the arousal level of driver by analyzing the subjective parameters. To clarify the design requirements, it is needed to evaluate the thermal comfort and the arousal level of drivers continuously, quantitatively and separately. So, we focused on the physiological parameters which can be measured continuously, we investigated the relationship between the thermal comfort sensation, the arousal level of drivers based on facial expression and the physiological parameters, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) when the indoor temperature changed. As a result, we showed that it is possible to evaluate the thermal comfort sensation and the arousal level of drivers quantitatively, continuously and separately by using those physiological parameters.
The aim of this study is to quantitatively evaluate the benefits of reused vehicle parts through a material flow approach. The energy consumed by new components for vehicle repairing can be saved. Moreover, an appropriate use of materials obtained from end-of-life vehicles (ELVs) contributes to waste reduction. Each reused part has different material compositions, weights, and demand; thus, the total benefit is evaluated using inventory analysis by introducing the definition of embodied energy and CO2 emissions. The Japanese ELV market was taken as a case study, and the results show that energy and CO2 saved by reusing parts are approximately 35.3 GJ and 1,887 kg-CO2 per vehicle, and 111 PJ and 6 MM ton-CO2 for the market in Japan. Finally, possible measures to boost the current benefits are discussed.