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As a crucial part of the intelligent transportation system, traffic signal control will realize the boundary control of the traffic area, it will also lead to delays and excessive fuel consumption when the vehicle is driving at the intersection. To tackle this challenge, this research provides an optimized control framework based on reinforcement learning method and speed guidance strategy for the connected vehicle network. Prior to entering an intersection, vehicles are focused on in a specific speed guidance area, and important factors like uniform speed, acceleration, deceleration, and parking are optimized. Conclusion, derived from deep reinforcement learning algorithm, the summation of the length of the vehicle’s queue in front of the signal light and the sum of the number of brakes are used as the reward function, and the vehicle information at the intersection is collected in real time through the road detector on the road network.

The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes. In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject. A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Road load data is an essential input to evaluate vehicle durability and strength performances. Typically, load case of pothole impact constitutes the major part in the development of structural durability. Meanwhile, misuse conditions like driving over a curb are also indispensable scenarios to complement impact strength of vehicle structures. This paper presents a methodology of establishing Multi-body Dynamics (MBD) full vehicle model in Adams/Car to acquire the road load data for use in durability and strength analysis. Furthermore, load level between durability and misuse conditions of the same Impact road was also investigated to explore the impact due to different driving maneuvers.

Automated driving is considered a key technology for reducing traffic accidents, improving road utilization, and enhancing transportation economy and thus has received extensive attention from academia and industry in recent years. Although recent improvements in artificial intelligence are beginning to be integrated into vehicles, current AD technology is still far from matching or exceeding the level of human driving ability. The key technologies that need to be developed include achieving a deep understanding and cognition of traffic scenarios and highly intelligent decision-making. Automated Vehicles, the Driving Brain, and Artificial Intelligenceaddresses brain-inspired driving and learning from the human brain's cognitive, thinking, reasoning, and memory abilities. This report presents a few unaddressed issues related to brain-inspired driving, including the cognitive mechanism, architecture implementation, scenario cognition, policy learning, testing, and validation.

Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement. Click here to access the full SAE EDGETM Research Report portfolio.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively.

In partly autonomous driving such as level 2 or level 3 automatic driving from SAE international classification, the switching of the driving right between the human driver and the machine plays an important role in the driving process of vehicle [1]. In this paper, the data collected from vehicle states and the driving behavior of drivers is completed via a simulator and self-report questionnaires. A fuzzy analytic network process (Fuzzy-ANP) model is developed to evaluate the driving capability of the drivers in real time from vehicle states due to its direct inherent link to the change of the driving state of drivers Moreover, in this model, the idea of group decision and multi-index fusion is adopted. The questionnaire is required to identify the experimental results from the simulator. The results show that the proposed Fuzzy-ANP model can evaluate the driving capability of the participants in real time accurately.

In this paper, a closed loop path planning and tracking control approach of collision avoidance for autonomous vehicle is proposed. The two-level model predictive control (MPC) is proposed for the path planning and tracking. The upper-level MPC is designed based on the simple vehicle kinematic model to calculate the collision-free trajectory and the potential field method is adopted to evaluate the collision risk and generate the cost function of the optimization problem. The lower-level MPC is the trajectory-tracking controller based on the vehicle dynamics model that calculates the desired control inputs. Finally the control inputs are distributed to steering wheel angle and motor torque via optimal control vectoring algorithm. Test cases are established on the Simulink/CarSim platform to evaluate the performance of the controller.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

A wet clutch is an established component in a conventional powertrain. It also finds a new role in electrified systems. For example, a wet clutch is utilized to couple or decouple an internal combustion engine from an electrically-driven drivetrain on demand in hybrid electric vehicles. In some electrical vehicle designs, it provides a means for motor speed reduction. Wet clutch control for those new applications may differ significantly from conventional strategy. For example, actuator pressure may be heavily modulated, causing the clutch to exhibit pronounced hysteresis. The clutch may be required to operate at a very high slip speed for unforeseen behaviors. A linear transfer function is commonly utilized for clutch control in automating shifting applications, assuming that clutch torque is proportional to actuator pressure. However, the linear model becomes inadequate for enabling robust control when the clutch behavior becomes highly nonlinear with hysteresis.

Innovations in mobility are built upon a management of complex interactions between sub-systems and components. A need for CAE tools that are capable of system simulations is well recognized, as evidenced by a growing number of commercial packages. However impressive they are, the predictability of such simulations still rests on the representation of the base components. Among them, a wet clutch actuator continues to play a critical role in the next generation propulsion systems. It converts hydraulic pressure to mechanical force to control torque transmitted through a clutch pack. The actuator is typically modeled as a hydraulic piston opposed by a mechanical spring. Because the piston slides over a seal, some models have a framework to account for seal friction. However, there are few contributions to the literature that describe the effects of seals on clutch actuator behaviors.

A wet clutch model is required in automotive propulsion system simulations for enabling robust design and control development. It commonly assumes Coulomb friction for simplicity, even though it does not represent the physics of hydrodynamic torque transfer. In practice, the Coulomb friction coefficient is treated as a tuning parameter in simulations to match vehicle data for targeted conditions. The simulations tend to deviate from actual behaviors for different drive conditions unless the friction coefficient is adjusted repeatedly. Alternatively, a complex hydrodynamic model, coupled with a surface contact model, is utilized to enhance the fidelity of system simulations for broader conditions. The theory of elastic asperity deformation is conventionally employed to model clutch surface contact. However, recent examination of friction material shows that the elastic modulus of surface fibers significantly exceeds the contact load, implying no deformation of fibers.

The article solves the problem of reducing electric power losses of the traction induction machine rotor to prevent its overheating in nominal and high-load modes. Electric losses of the rotor power are optimized by the stabilization of the main magnetic flow of the electric machine at a nominal level with the amplitude-frequency control in a wide range of speeds and increased loads. The quasi-independent excitation of the induction machine allows us to increase the rigidity of mechanical characteristics, decrease the rotor slip at nominal loads and overloads and significantly decrease electrical losses in the rotor as compared to other control methods. The article considers the technology of converting the power of individual phases into a single energy flow using a three-phase electric machine equivalent circuit and obtaining an energy model in the form of equations of instantaneous active and reactive power balance.

The dynamic trajectory planning problem for automatic vehicles in complex traffic scenarios is investigated in this paper. A hierarchical motion planning framework is developed to complete the complex planning task. An improved dangerous potential field in the curvilinear coordinate system is constructed to describe the collision risk of automatic vehicles accurately instead of the discrete Gaussian convolution algorithm. At the same time, the driving comfort is also considered in order to generate an optimal, smooth, collision-free and feasible path in dynamics. The optimal path can be mapped into the Cartesian coordinate system simply and conveniently. Furthermore, a velocity profile considering practical vehicle dynamics is also presented to improve the safety and the comfort in driving. The effectiveness of the proposed dynamic trajectory planning is verified by numerical simulation for several typical traffic scenarios.

In-vehicle infotainment (IVI) platforms are getting increasingly connected. Besides OEM apps and services, the next generation of IVI platforms are expected to offer integration of third-party apps. Under this anticipated business model, vehicular sensor and event data can be collected and shared with selected third-party apps. To accommodate this trend, Google has been pushing towards standardization among proprietary IVI operating systems with their Android Automotive platform which runs natively on the vehicle’s IVI platform. Unlike Android Auto’s limited functionality of display-projecting certain smartphone apps to the IVI screen, Android Automotive will have access to the in-vehicle network (IVN), and will be able to read and share various vehicular sensor data with third-party apps. This increased connectivity opens new business opportunities for both the car manufacturer as well as third-party businesses, but also introduces a new attack surface on the vehicle.

A wet clutch continues to play a critical role for step-ratio automatic transmissions and finds new utilities in hybrid and electrified propulsion systems. A torque transfer function is often employed in practice for sophisticated clutch slip controls. It provides a simple, yet practical framework to represent clutch torque as a function of actuator force. An accurate transfer function is also increasingly desired in today's vehicle design process to enable upfront assessment of clutch controls through simulations. The most common approach is based on Coulomb's linear friction model, where the coefficients are adaptively identified based on vehicle data. However, it is generally difficult to tune Coulomb's model for hydrodynamic behaviors even if the reference vehicle data are available. It also remains a challenge to produce in-vehicle clutch behaviors on a component test bench to determine realistic transfer function before prototype vehicles are built.

In order to improve the performance of electro-hydraulic composite braking system of hybrid electric vehicle (HEV), a new type of plug-in HEV with dual motor was taken as the research object. The model of motor loss was built to achieve maximum motor efficiency, and the hydraulic braking system model, which can dynamically control pressure, was built. Based on the optimization of a motor’s continuously variable transmission (CVT) joint efficiency, the real-time optimal allocation strategy based on threshold method and cooperative control strategy of the electro-hydraulic composite braking system were brought out to recover most of the regenerative energy under the premise of ensuring safety. The model was built to verify the performance by AMESim-Simulink.

In order to improve the steering stability of the dual-motor drive electric vehicle, Taking the yaw rate and the sideslip angle as the control variables, Using the improved two degree of freedom linear dynamic model and seven degree of freedom nonlinear vehicle dynamics model, The hierarchical structure is used to establish the dual-motor drive electric vehicle steering stability control strategy which consist of the upper direct yaw moment decision-making layer based on the sliding mode controller and the lower additional yaw moment distribution layer based on the optimization theory. The Matlab/Simulink-Carsim joint simulation platform was built. The control strategy proposed in this paper was simulated and verified under the snake test condition and double-line shift test condition.