Yoshitaka Marumo

Steering control of motorcycles using steer-by-wire system

This study proposes a steering control method to improve motorcycle handling and stability. Steer-by-wire (SBW) technology is applied to the motorcycles steering system to remove characteristic difficulties of vehicle maneuvers. By examining computer simulation using a simplified motorcycle model, the actual rolling angle of the SBW motorcycle is controlled to follow the desired rolling angle intended by the rider. A state feedback control such as linear quadratic control gives the SBW vehicle a good follow-through performance compared with proportional-derivative control because it can decouple rolling motion from the other motions, which affect the rolling motion in the strongly coupled motorcycle system.

Control effects of steer-by-wire system for motorcycles on lane-keeping performance

This study discusses the control effects of the steer-by-wire (SBW) system for motorcycles on the lane-keeping performance by examining computer simulation with a rider-vehicle system which consists of a simplified vehicle model, a rider control model and the controller of the SBW system. The SBW system, which compensates the rolling angle deviation between the desired rolling angle intended by the rider and the actual rolling angle, improves the lane-keeping performance of the rider-vehicle system under the steering torque disturbance. The SBW system is, on the other hand, not effective in the lane-keeping performance under the lateral force disturbance. In addition, the lane-keeping assistance (LKA) system is applied to the SBW system and the cooperativeness of the SBW and the LKA systems is examined. The LKA system improves the lane-keeping performance of the SBW system under not only the steering torque disturbance but also the lateral force disturbance.

Study on automatic path tracking using virtual point regulator

This study discusses a vehicle path tracking strategy using a virtual point ahead of the vehicle to trace the desired path. Although the vehicle center of gravity can also be employed for the tracking, it is impossible to design the yaw control independently from the controlled lateral motion. By contrast, the yaw motion, as well as the lateral motion, can be controlled to obtain its own desired performance when the virtual point is used for the tracking, although this applies with certain reservations. Additionally, employing the virtual point also secures good robust stability and disturbance suppression performance of the control system.

Fault Detection of Railway Vehicles Using Multiple Model Approach

This paper describes the estimation algorithm of the fault detection of railway vehicles. This algorithm is formulated based on the interacting multiple-model (IMM) algorithm. IMM algorithm which choose probable model from number of models is applied to the fault detection. In IMM method, changes of the systems structure and the systems parameters are called mode. We provide several suspension failure modes and sensor failure modes for the fault detection. The mode probabilities and states of vehicle suspension are estimated based on Kalman filter (KF). This algorithm is evaluated in simulation examples. Simulation results show that the algorithm is effective for on-board fault detection of the railway vehicle suspension

Condition Monitoring and Fault Detection of Railway Vehicle Suspension Using Multiple-Model Approach

Abstract This paper demonstrates the possibility to detect suspension failures of railway vehicles using a multiple-model approach from on-board measurement data. The railway vehicle model used includes the lateral and yaw motions of the wheelsets and bogie, and the lateral motion of the vehicle body, with sensors measuring the lateral acceleration and yaw rate of the bogie, and lateral acceleration of the body. The detection algorithm is formulated based on the Interacting Multiple-Model (IMM) algorithm. The IMM method has been applied for detecting faults in vehicle suspension systems in a simulation study. The mode probabilities and states of vehicle suspension systems are estimated based on a Kalman Filter (KF). This algorithm is evaluated in simulation examples. Simulation results indicate that the algorithm effectively detects on-board faults of railway vehicle suspension systems.

Effects of multiple axles on the lateral dynamics of multi-articulated vehicles

This paper describes an analytical study of the lateral dynamics of multi-articulated vehicles with multiple axles. A linear planar model of vehicle dynamics is adopted for multiple-axle vehicle combinations with an optional number of trailers. Two tractor and double-trailer combinations are examined for their directional stability and response. Non-oscillatory stability and steering sensitivity in steady-state turning and lane changing are analysed using a stability factor of multiple-axle vehicle combinations. Off-tracking in the steady-state turning of multiple-axle vehicle combinations is also analysed. Numerical calculations for oscillatory stability, steering sensitivity, and off-tracking are presented for multiple-axle vehicle combinations.

Fault detection of railway vehicle suspensions using multiple model approach

This paper describes the estimation algorithm of the fault detection of railway vehicles. This algorithm is formulated based on the interacting multiple-model (IMM) algorithm. IMM algorithm which choose probable model from number of models is applied to the fault detection. In IMM method, changes of the systems structure and the systems parameters are called mode. We provide several suspension failure modes and sensor failure modes for the fault detection. The mode probabilities and states of vehicle suspension are estimated based on Kalman filter (KF). This algorithm is evaluated in simulation examples. Simulation results show that the algorithm is effective for on-board fault detection of the railway vehicle suspension.

Evaluation of braking behaviour for train drivers using phase-plane trajectories

This study analyses braking behaviour of train drivers and estimates drivers’ mental condition using a train simulator. The velocity deviation recognised by drivers is defined by the difference between the present vehicle velocity and the desired velocity that enables the vehicle to stop at the desired position if the present deceleration is kept constant. Observing the relation between the velocity deviation and the braking operation enables the detection of abnormal driving behaviour. The phase-plane trajectory of the velocity deviation and the brake command with mental workload indicates a large velocity deviation and repetition of the braking operation, while the trajectory without mental workload indicates that both the velocity deviation and the brake command gradually approach the origin of the coordinate axes.

A New Approach to Green Light Optimal Speed Advisory (GLOSA) Systems and Its Limitations in Traffic Flows

The use of Green Light Optimal Speed Advisory (GLOSA) systems is currently considered one of the key applications for achieving more stable, environmentally friendly, and efficient traffic flows in the vicinity of signalized intersections. This paper addresses a driver advisory system that provides an efficient driving strategy based on our GLOSA system and evaluates its impact on traffic flow characteristics. Assuming five levels of traffic demand, traffic simulations were carried out to investigate the performance of the system in terms of the travel time, fuel consumption, and carbon dioxide (CO2) emissions of vehicles entering and exiting an artificial corridor. Our numerical analysis showed that our GLOSA system performs well in traffic flows where the arriving demand is less than 400 to 500 vehicles per hour. In other situations, it increases congestion, impedes efficiency, and significantly worsens the environment.

Mitigation of rear-end collision risk based on intent inference of preceding car's deceleration behavior

Rear-end collisions occasionally cause severe multiple-collision accidents, especially in high-density and high-speed traffic. Avoiding the rear-end collisions is one of the critical issues since this type of collision has been the most significant factor among all types of traffic accident for many years. This research aims to develop a driver assistance system that informs the inferred intention of preceding cars deceleration behavior to the following vehicle through an interface based on a wind shield display (W SD). Assuming four vehicles forming a longitudinal platoon, a system was developed in which the deceleration intent of the 3rd vehicle is inferred and informed to the 4th vehicle driver by the WSD. A driving simulator (DS) experiment was carried out to evaluate the performance including the collision risk of the 4th vehicle with and without the system. A series of the experiments showed that the proposed system was significantly effective to mitigate the collision risk even under emergency deceleration scenarios.