Road boundary detection and tracking for structured and unstructured roads using a 2D lidar sensor

Road boundaries can give useful information for evaluating safe vehicle paths in intelligent vehicles. Much previous research has studied road boundary detection, using different types of sensors such as vision, radar, and lidar. Lidar sensors, in particular, show advantages for road boundary extraction including high resolution and wide field of view. However, none of the previous studies examined the problem of detecting road boundaries when roads could be either structured or unstructured. In this study, we developed a road boundary detection and tracking algorithm using lidar sensing for both structured and unstructured roads. The algorithm extracts road features as line segments in polar coordinates relative to the lidar sensor. The extracted road features are then tracked with respect to a vehicle’s local coordinates using a nearest neighbor filter. The proposed algorithm accurately detected the road boundaries regardless of the road type.     

The influence of personality on acceptability of sustainable transport policies

Previous research has shown that fairness, infringement on freedom, and perceived effectiveness are determinants of transport pricing acceptability. In the present study we investigate determinants of acceptability of environmental (carbon) taxation for which trust in government and environmental concern are additional determinants. Carbon taxation is an extension of fuel taxes and may thus be viewed as transport pricing. Our main focus is on the role played by personality traits. Structural equation modeling reveals that acceptability is related to the personality traits extraversion, agreeableness, and conscientiousness. Extraverted individuals have higher levels of trust in government which leads to higher acceptability. Also correlations between agreeableness and conscientiousness as well as environmental problem awareness and personal norm are observed. We discuss strategies for effective marketing of transportation policies considering how acceptability is related to personality traits.     

An experimental study of the effect of low-noise wheels in reducing noise and vibration

Wheel/rail interaction is a major source of railway noise. A low-noise wheel structure is developed and its effect on noise reduction is investigated. This low-noise wheel employs a rubber material inserted into the steel rim or mounted on the wheel surface. The low-noise wheel has low stiffness and a high-damping ratio compared to a solid wheel. Measurement shows that it reduces rolling and squealing. It turns out that a subway line with the proposed wheel could reduce its interior noise level by 4–5 dB(A) and vehicle vibration level by 7–8 dB. While the proposed structure seems promising in noise reduction for railway vehicles, the endurance and cost effectiveness of the low-noise wheel are yet to be verified.     

Analysis of a regenerative braking system for Hybrid Electric Vehicles using an Electro-Mechanical Brake

The regenerative braking system of the Hybrid Electric Vehicle (HEV) is a key technology that can improve fuel efficiency by 20∼50%, depending on motor size. In the regenerative braking system, the electronically controlled brake subsystem that directs the braking forces into four wheels independently is indispensable. This technology is currently found in the Electronic Stability Program (ESP) and in Vehicle Dynamic Control (VDC). As braking technologies progress toward brake-by-wire systems, the development of Electro-Mechanical Brake (EMB) systems will be very important in the improvement of both fuel consumption and vehicle safety. This paper investigates the modeling and simulation of EMB systems for HEVs. The HEV powertrain was modeled to include the internal combustion engine, electric motor, battery and transmission. The performance simulation for the regenerative braking system of the HEV was performed using MATLAB/Simulink. The control performance of the EMB system was evaluated via the simulation of the regenerative braking of the HEV during various driving conditions.     

Belt routing and safety assessment through computer simulation

Most of the research on safety belt systems has involved crash simulation that only considered a dynamic human model. However, belt routing analysis, usually known as comfort level estimation, is also an important factor in safety belt design, considering that serious injuries of the abdominal region result from the infiltration of a belt into the neck or the chest. Thus, safety belt evaluations using kinematic human models are also needed. In this paper, a belt fit simulation method is suggested. Using the proposed process, both comfort and safety analyses can be performed under the same conditions continuously, and thus the safety belt design parameters, such as the location of anchor points, dummy posture and etc., can be evaluated. In conclusion, this computer process enables a belt system design to reduce injuries.     

Development of algorithms for commercial vehicle mass and road grade estimation

Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.     

Study of RCM-based maintenance planning for complex structures using soft computing technique

To guarantee the efficiency of maintenance strategies for a complex structure, safety and cost limitations must be considered. This research introduces RCM-based (Reliability Centered Maintenance) life cycle optimization for reasonable maintenance. The design variable is the reliability of each part, which consists of a complex structure, while the objective is to minimize the total cost function in order to maintain the system within the desired system reliability. This research constructs the cost function that can reflect the current operating condition and maintenance characteristics of individual parts by generating essential cost factors. To identify the optimal reliability of each component in a system, this paper uses a Neuro-Evolutionary technique. Additionally, this research analyzes the reliability growth of a system by using the AMSAA (Army Material Systems Analysis Activity) model to estimate the failure rate of each part. The MTBF (Mean Time Between Failure) and the failure rate of the whole system, which is responding to the individual parts, are estimated based on the history data by using neural networks. Finally, this paper presents the optimal life cycle of a complex structure by applying the optimal reliability and the estimated MTBF to the RAMS (Reliability, Availability, Maintainability, and Safety) algorithm.     

Performance analysis of a CVT clutch system for a hybrid electric vehicle

In this study, the performance of a CVT clutch system for a hybrid electric vehicle was investigated. To analyzed the vehicle performance at restart, the restart delay and driveshaft torque was investigated by simulations and experiments. It was found from the simulation results that the vehicle restart response depends on the clutch pressure buildup time to the point where the clutch torque begins to overcome the vehicle road load, and driving comfort at restart is directly related to the rate change of the clutch pressure.