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Decision-making of lane-change for autonomous vehicles faces challenges due to the behavioral differences among human drivers in dynamic traffic environments. To enhance the performances of autonomous vehicles, this paper proposes a game theoretic decision-making method that considers the diverse Social Value Orientations (SVO) of drivers. To begin with, trajectory features are extracted from the NGSIM dataset, followed by the application of Inverse Reinforcement Learning (IRL) to determine the reward preferences exhibited by drivers with distinct Social Value Orientation (SVO) during their decision-making process. Subsequently, a reward function is formulated, considering the factors of safety, efficiency, and comfort. To tackle the challenges associated with interaction, a Stackelberg game model is employed.

Today, commercially available drones have limited use-cases in the rapidly evolving community. However, with advances in drone and software technology, it is possible to utilize these aerial machines to solve problems in a variety of industries such as mining, medical, construction, and law enforcement. For example, in order to reduce time of investigation, Indiana State Police are currently utilizing ad-hoc commercial drones to reconstruct crash scenes for insurance and legal purposes. In this paper, we illustrate how to effectively integrate drones for in-vehicle services and real-time prediction for automotive applications. In order to accomplish this, we first integrate simpler controls such as voice-commands to control the drone from the vehicle. Next, we build smart prediction software that monitors vehicle behavior and reacts in real-time to collisions.

This paper presents an optimal cooperative path planning method considering driver’s driving intention for shared control to address target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial cooperative path profiles of the cooperative path in the Frenet-based coordinate system are induced by considering the driver’s lane-changing intention recognized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization techniques. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach.

In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle.

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML within software has on the ISO 26262 safety lifecycle and ask what could be done to address them. We then provide a set of recommendations on how to adapt the standard to better accommodate ML.

Whole-body vibration (WBV) is associated with several adverse health and safety outcomes including low-back pain (LBP) and driver fatigue. The objective of this study was to evaluate the efficacy of three commercially-available air-suspension truck seats for reducing truck drivers’ exposures to WBV. Seventeen truck drivers operating over a standardized route were recruited for this study and three commercially-available air suspension seats were evaluated. The predominant, z-axis average weighted vibration (Aw) and Vibration Dose Values (VDV) were calculated and normalized to represent eight hours of truck operation. In addition, the Seat Effective Amplitude Transmissibility (SEAT), the ratio of the seat-measured vibration divided by the floor-measured vibration, was compared across the three seats. One seat had significantly higher on-road WBV exposures whereas there were no differences across seats in off-road WBV exposures.

Energy management strategies greatly influence the power performance and fuel economy of series hybrid electric tracked bulldozers. In this paper, we present a procedure for the design of a power management strategy by defining a cost function, in this case, the minimization of the vehicle’s fuel consumption over a driving cycle. To explore the fuel-saving potential of a series hybrid electric tracked bulldozer, a dynamic programming (DP) algorithm is utilized to determine the optimal control actions for a series hybrid powertrain, and this can be the benchmark for the assessment of other control strategies. The results from comparing the DP strategy and the rule-based control strategy indicate that this procedure results in approximately a 7% improvement in fuel economy.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

A three dimensional IR-TRACC (Infrared Telescope Rod for Assessment of Chest Compression) was designed for the Test Device for Human Occupant Restraint (THOR) in recent years to measure chest deflections. Due to the design intricateness, the deflection calculation from the measurements is sophisticated. An algorithm was developed in this paper to calculate the three dimensional deflections of the chest. The algorithm calculates the compression and also converts the results to the local spine coordinate system so that it can correlate with the Post Mortem Human Subject (PMHS) measurements for injury calculation. The method was also verified by a finite element calculation for accuracy, comparing the calculation from the corresponding model output and the direct point to point measurements. In addition, the IR-TRACC calibration methods are discussed in this paper.

A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

A compact sized vehicle that has a narrow track could solve problems caused by vehicle congestion and limited parking spaces in a mega city. Having a smaller footprint reduces the vehicle's total weight which would decrease overall vehicle power consumption. Also a smaller and narrower vehicle could travel easily through tight and congested roads that would speed up the traffic flow and hence decrease the overall traffic volume in urban areas. As an additional benefit of having a narrow track length, a driver can experience similar motorcycle riding experience without worrying about bad weather conditions since a driver sits in a weather protected cabin. However, reducing the vehicle's track causes instability in vehicle dynamics, which leads to higher possibility of rollovers if the vehicle is not controlled properly. A three wheel personal vehicle with an active tilting system is designed in MapleSim.

Parameter identification of a math-based spark-ignition engine model is studied in this paper. Differential-algebraic equations governing the dynamic behavior of the engine combustion model are derived using a quasi-dimensional modelling scheme. The model is developed based on the two-zone combustion theory with turbulent flame propagation through the combustion chamber [1]. The system of equations includes physics-based equations combined with the semi-empirical Wiebe function. The GT-Power engine simulator software [2], a powerful tool for design and development of engines, is used to extract the reference data for the engine parameter identification. The models is GT-Power are calibrated and validated with experimental results; thus, acquired data from the software can be a reliable reference for engine validation purposes.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.

Physical rig testing of a vehicle is often undertaken to obtain experimental data that can be used to ensure a mathematical model is an accurate representation of the vehicle under study. Kinematics and Compliance (K&C) testing is often used for this purpose. The relationship between the hard point locations and compliance parameters, and K&C characteristics of a suspension system is complex, and so automating the process to correlate the model to the test data can make the exercise easier, faster and more accurate than hand tuning the model. In this work, such a process is developed. First, the model parameters are adjusted, next a simulation is run, before the results are read and post processed. This automation processed is used in conjunction with an optimization procedure to carry out the K&C correlation.

Significant improvement in fuel consumption, torque delivery and emission could be achieved through flexible control of the valve timings, duration and lift. In most existing electro-hydraulic variable valve actuation systems, the desired valve lift within every engine cycle is achieved by accurately controlling of the solenoid-valve opening interval; however, due to slow response time, precision control of these valves is difficult particularly during higher engine speeds. In this paper a new lift control strategy is proposed based on the hydraulic supply pressure and flow control. In this method, in order to control the peak valve lift, the hydraulic pump speed is precisely controlled using a two-input gearbox mechanism. This eliminates the need for precision control of the solenoid valves opening interval within every cycle.

An electric vehicle model has been developed with four direct-drive in-wheel motors. A high-level vehicle stability controller is proposed, which uses the principles of fuzzy logic to determine the corrective yaw moment required to minimize the vehicle sideslip and yaw rate errors. A genetic algorithm has been used to optimize the parameters of the fuzzy controller. The performance of the controller is evaluated as the vehicle is driven through a double-lane-change maneuver. Preliminary results indicate that the proposed control system has the ability to improve the performance of the vehicle considerably.

Vehicles designed for the Baja SAE competition operate on challenging off-road terrain and may be required to withstand accidental impacts with other vehicles and obstacles. Although significant injuries are not commonly observed in this competition, it is important to understand the performance of these vehicles in crash scenarios to optimize frame design and vehicle performance. A finite element model comprising the vehicle chassis and associated subsystem weights, a Hybrid III occupant, and safety systems was developed to evaluate vehicle impact performance in frontal crash. Impacts velocities up to 36 kph were considered, and no significant risk of head, neck or thoracic injury was predicted. Neck injury (as predicted by Nij) and chest acceleration were found to be the most critical, reaching 66% and 75% of their threshold values, respectively, in the most severe crashes considered.

A serpentine flow channel can be considered as neighboring channels connected in series, and is one of the most common and practical channel layouts for PEM fuel cells since it ensures the removal of liquid water produced in a cell with excellent performance and acceptable parasitic load. During the reactant flows along the flow channel, it can also leak or cross directly to the neighboring channel via the porous gas diffusion layer due to the high pressure gradient caused by the short distance. Such a cross flow leads to a larger effective flow area resulting in a substantially lower amount of pressure drop in an actual PEM fuel cell compared to the case without cross flow. In this work, an analytical solution is obtained for the cross flow in a PEM fuel cell with a serpentine flow channel based on the assumption that the velocity of cross flow is linearly distributed in the gas diffusion layer between two successive U-turns.

Motorized shoulder belt tensioning is an occupant protection technology that has promise to reduce automotive crash injuries. The objective of this study was to model the response of a diverse forward-leaning occupant population (6-year-old child, 5th female, 50th male, 95th male) to shoulder belt tensioning during straight line pre-crash braking. The lumped mass model was based on experimental volunteer data for motorized shoulder belt tensioning gathered in a previous quasistatic study. The three dimensional model incorporated the biomechanical properties of the occupant populations, a motorized shoulder belt tensioner (DC motor and controller) and shoulder belt webbing models. Model validation was achieved against the volunteer experiments for angular torso position, torso velocity and shoulder belt moment applied to the torso.