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Hydrostatic (hydraulic hybrid) drives have demonstrated energy efficiency and emissions reduction benefits. This paper investigates the potential of an independent hydrostatic wheel drive system for implementing a traction-based vehicle lateral stability control system. The system allows an upper level vehicle stability controller to produce a desired corrective yaw moment via a differential distribution of torque to the independent wheel motors. In cornering maneuvers that require braking on any one wheel of the vehicle, the motors can be operated as pumps for re-generating energy into an on-board accumulator. This approach avoids or reduces activation of the friction brakes, thereby reducing energy waste as heat in the brake pads and offering potential savings in brake maintenance costs. For this study, a model of a 4×4 hydrostatic independent wheel drive system is constructed in a causal and modular fashion and is coupled to a 7 DOF vehicle handling dynamics model.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

The presented manuscript discusses the implementation of the pulsed-thermography technique for the non-intrusive evaluation of automotive parts. The study discusses the fundamentals of static and dynamic thermography through examples and case studies. Furthermore, the proposed pulse thermography system is analyzed in terms of hardware calibration i.e. pulse duration and intensity and the detector effect on the time and the spatial resolutions. Current thermography processing codes and techniques are also described and critiqued, with new processing subroutines proposed; one based on self-referencing thersholding. Additionally, new trends in infrared and visible sensors fusion are presented.

Rollover prevention is now part of complete vehicle stability control systems for many vehicles. Given that rollover is predominantly associated with vehicles with high centers of gravity, the targeted vehicles for rollover protection include medium and heavy duty commercial vehicles. Unfortunately, the chassis designs of these vehicles are often so compliant in torsion that the ends of the vehicles may have significantly different roll responses at any given time. The potential need to assess and correct for the roll behavior of the front and rear ends of the vehicle is the subject of this paper. Most rollover mitigation research to date has used rigid chassis assumptions in modeling the vehicle. This paper deals with the roll control of vehicles with torsionally flexible chassis based on a yaw-correction system.

The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

The NOx reduction performance under lean conditions over γ-alumina was evaluated using a micro-reactor system and a non-thermal plasma-equipped bench test system. Various alumina samples were obtained from alumina manufacturers to assess commercial alumina materials. In addition, γ-alumina samples were synthesized at Caterpillar with a sol-gel technique in order to control alumina properties. The deNOx performances of the alumina samples were compared. The alumina samples were characterized with analytical techniques such as inductively coupled plasma (ICP) emission spectroscopy, temperature programmed desorption (TPD) and surface area measurements (BET) to understand physical and chemical properties. The information derived from these techniques was correlated with the NOx reduction performance to identify key parameters of γ-alumina for optimizing materials for lean-NOx and plasma assisted catalysis.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

This paper proposes a new vehicle stability control method that quantifies and uses the level of lateral force saturation on each axle/wheel of a vehicle. The magnitude of the saturation, which can be interpreted as a slip-angle deficiency, is determined from on-line estimated nonlinear tire lateral forces and their linear projections that use estimates of the cornering stiffness. Once known, the saturation levels are employed in a saturation balancing control structure that biases the drive torque to either the front or rear axles/wheels with the goal of minimizing excessive under- or over-steer, thereby stabilizing the vehicle. The method is particularly suited for a vehicle with an independent wheel drive system. Furthermore, the method can be used in conjunction with a direct yaw-moment controller to obtain enhanced stability and responsiveness.

A new press/die system for restraining force control has been developed in order to facilitate an increased level of process control in sheet metal forming. The press features a built-in system for controlling drawbead penetration in real time. The die has local force transducers built into the draw radius of the lower tooling. These sensors are designed to give process information useful for the drawbead control. This paper focuses on developing models of the drawbead actuators and the die shoulder sensors. The actuator model is useful for developing optimal control methods. The sensor characterization is necessary in order to develop a relationship between the raw sensor outputs and a definitive process characteristic such as drawbead restraining force (DBRF). Closed loop control of local specific punch force is demonstrated using the die shoulder sensor and a PID controller developed off-line with the actuator model.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.

Performance data offers a powerful tool for system condition assessment and health monitoring. In most applications, a host of various types of sensors is employed and data on key parameters (describing the system performance) is compiled for further analysis and evaluation. In ensuring the adequacy of the data acquisition process, two important questions arise: (1) is the complied data robust and reasonable in representing the system parameters; and (2) is the duration of data acquisition adequate to capture a favorable percentage (say for example 90%) of the critical values of a given system parameter? The issue related to the robustness and reasonableness of data can be addressed through known values for key parameters of the system. This is the information that is not often available. And as such, methods based on trends in a given system parameter, expected norms, the parameter's relation with other known parameters, and simulations can be used to assure the quality of the data.

Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position).

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.