Development of a navigation algorithm with dead reckoning for unmanned ground vehicles

A navigation algorithm is indispensable for Unmanned Ground Vehicles (UGVs). During driving, UGVs follow a global path. In this study, we propose a navigation algorithm using Real Time Kinematic (RTK)-Differential Global Positioning System (DGPS) units and encoders to complement global path planning. Sometimes GPS systems lose their signals and receive inaccurate position data due to many factors, such as edifice and barrier obstructions. This paper shows that GPS deviations can be solved using a Dead Reckoning (DR) navigation method with encoders and that position errors can be decreased through the use of RTK-DGPS units. In addition to this method, we will introduce a new waypoint update algorithm and a steering algorithm using RTK-DGPS units.     

Modified lateral control of an autonomous vehicle by look-ahead and look-down sensing

This paper presents a modified lateral control method for an autonomous vehicle with both look-ahead and look-down sensing systems. To cope with sensor noise and modeling uncertainty in the lateral control of the vehicle, a modified LMI-based H lateral controller was proposed, which uses the look-ahead information of the lateral offset error measured at the front of vehicle and the look-down information of the vehicle yaw angle error between the reference lane and the centerline of the vehicle. To verify the safety and the performance of the lateral control, a scaled-down vehicle was developed, and the positioning of the vehicle was estimated with USAT. The proposed controller, which uses both look-ahead and look-down information, was tested for lane changing and reference lane tracking with both simulation and experiment. The simulation and experimental results show that the proposed controller has better tracking and handling performance compared with a controller that uses only the look-ahead information of the target heading angle error.     

License plate extraction method for identification of vehicle violations at a railway level crossing

The primary cause of most railroad accidents is vehicle entry into railway level crossings despite warning messages. To identify drivers who violate railway level crossing regulations, vehicle license plate recognition can be applied at railway level crossings. The purpose of this paper is to present an effective method for extracting the license plate region from vehicle images taken at railway level crossings. The method proposed in this paper uses the variation in the gray-level values across the image of a license plate. For license plate region extraction, the character region is first recognized by identifying the character width and the difference between the background region and the character region. The license plate region is then extracted by finding the inter-character distance in the plate region. In addition, the license plate type is identified by the difference in the gray-level value between the background region and the character region. The proposed method is effective in solving the current challenges in extracting the license plate region from the damaged frames of license plates issued for domestic use, including new types of license plates. According to the experimental results, the proposed method yields a high extraction rate of 99.5% for vehicle license plates.     

Design framework for FlexRay network parameter optimization

Because the FlexRay protocol has more than 70 configuration parameters and these parameters correlate with each other, designing a FlexRay network is a complex and difficult task. In this study, we propose a design framework that optimizes the two main FlexRay network parameters that are highly relevant to the application algorithm. The design process is composed of two steps for optimizing parameters. In the first step, the static slot length is optimized using a frame-packing algorithm. This algorithm binds network signals into static frames based on their periods and signal groups. In the second step, the communication cycle length is optimally designed with frame-scheduling algorithm and worst-case reponse time analysis. Based on the frame-scheduling algorithm, the response times are analyzed. The proposed design framework was applied to a unified chassis control system as a case study, and the analytical results were verified.     

Comprehensive lateral driver model for critical maneuvering conditions

A new comprehensive driver model is presented for critical maneuvering conditions with more accurate dynamic control performance. In order to achieve a safe maneuvering mode, a new path planning scheme to maintain stability of the vehicle was designed. A new steering strategy, considering the errors of vehicle position and yaw angle between the real track and the planned path, was established to obtain the steering angle. Therefore, the vehicle can be adjusted to accurately follow the desired path with the driver model, and the stability of the vehicle and the smoothness of the steering angle input were comprehensively considered. Simulation results were used to validate the control performance in comparison with the optimal preview driver model proposed by Macadam.     

Vehicle interior noise model based on a power law

Identifying the components of a vehicle’s interior noise is important in many phases of the noise, vibration, and harshness (NVH) development process. Many test methods that have been widely used in the automobile industry to separate noise sources are based on system identification methods in the frequency domain. However, none of the frequency response function-based methods can directly estimate the wind noise component. In this article, an analytical model for the interior noise level based on a simple power law was developed. It was assumed that the mean squared acoustic pressure for the interior noise could be obtained by summing up those of the wind noise, road noise, and background noise. The wind noise and road noise were further assumed to depend only on wind speed and the vehicle’s driving speed, respectively, and to follow a simple power law. The resulting analytical model includes five parameters that can be optimized for the vehicle and the road. The validity of the model was verified by using data obtained from cruise tests performed on a proving ground for cruise speeds ranging from 40 km/h to 130 km/h. The model is applied to the overall and 1/3-octave bands of interior noise and is shown to describe the data trends fairly well. For the test vehicle used in the present work, the overall mean squared pressures for the wind and road noise components are shown to be proportional to the wind speed to the 5.8 power and to the driving speed to the 3.4 power, respectively.     

Advance-F automatic car-following model and its traffic characteristics

This study develops a longitudinal control model for automatic car-following with one switch and two controllers. The switch is operated using fuzzy logic to activate acceleration or trigger braking. The two controllers activate the acceleration pedal and the brake system and use reference adaptive control theory. A simulation is utilized to evaluate the acceptability of the proposed model and to compare the traffic characteristics between machine driving (by the proposed model) and human driving (as represented by the GM-V model). The simulation results indicate that this model is acceptable and is both safe and efficient for use in traffic. However, the automatic car-following system is more aggressive than humans are, which may be the primary challenge in implementation.     

Piston-temperature measuring system using electromagnetic induction with verification by a telemetry system

The development of an inner-piston-chamber temperature measurement system is a necessary step in engine development or when solving other fundamental problems related to automotive engines. There are various pre-existing measurement methods available, e.g., the linkage method, piston telemetry, templog, and the electromagnetic induction method. In this study, we first redesigned the coil sensor used in the electromagnetic induction method using PEEK and then used Taguchi methods to reduce the number of experiments in the development process and finally utilized piston telemetry via Bluetooth to verify the precision and accuracy of the redesigned PEEK coil sensor and electromagnetic induction method. The results displayed a reproducibility within 0.5 degrees and an accuracy within 2 degrees Celsius.     

Parameter identification for a LuGre model based on steady-state tire conditions

The purpose of this study was to effectively identify parameters for a LuGre friction model based on experimental measures. In earlier work related to this study (Yang et al., 2009), which was based on the characters of polygonal wear (Sueoka and Ryu, 1997), we showed a frictional vibration model for a mass on a moving belt. This model reflected lateral vibrations caused by velocity and toe-in angle. An important aspect of the present study is the improved friction model. A previous friction model, which divided the process into four parts, expressed the sable excited vibration well but failed to reflect the hysteresis loop change when vehicles accelerated or decelerated continuously. A LuGre friction model can solve this problem, but several model parameters must be obtained experimentally. We measured contact width and length of tires as vertical pressure changed; this provided a theoretical basis for apparent stiffness of a unit of tire tread. Based on tire data from Bakker E’s article in a SAE paper from 1987, we identified the Stribeck exponent and Stribeck velocity in LuGre. Then, the results were implemented in a vibration system that verified the rationality of the data.