Search Results

Landing of spacecraft on Lunar or Martian surfaces is the last and critical step in inter planetary space missions. The atmosphere on earth is thick enough to slow down the craft but Moon or Mars does not provide a similar atmosphere. Moreover, other factors such as lunar dust, availability of precise onboard navigational aids etc would impact decision making. Soft landing meaning controlling the velocity of the craft from over 6000km/h to zero. If the craft’s velocity is not controlled, it might crash. Various onboard sensors and onboard computing power play a critical role in estimating and hence controlling the velocity, in the absence of GPS-like navigational aids. In this paper, an attempt is made using visual onboard sensor to estimate the velocity of the object. The precise estimation of an object's velocity is a vital component in the trajectory planning of space vehicles, particularly those designed for descent onto lunar or Martian terrains, such as orbiters or landers.

A GE Aviation Systems report documents that the National Oceanic and Atmospheric Administration (NOAA) provided weather forecast data has a bias of 15 knots and a standard deviation of 13.3 knots for the 40 flights considered for the research. It also had a 0.47 bias in the temperature with a standard deviation of 0.27. The temperature errors are not as significant as the wind. There is a potential opportunity to reduce the operational cost by improving the weather forecast. The flight management system (FMS) currently uses the weather forecast, available before takeoff, to identify an optimized flight path with minimum operational costs depending on the selected speed mode. Such a flight plan could be optimum for a shorter flight because these flight path planning algorithms are very less susceptible to the accuracy of the weather forecast.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

Brake pulsation is a low frequency vibration phenomenon in brake judder. In this study, a simulation approach has been developed to understand the physics behind brake pulsation employing a full vehicle dynamics CAE model. The full vehicle dynamic model was further studied to understand the impact of suspension tuning variation to brake pulsation performance. Brake torque variation (BTV) due to brake thickness variation from uneven rotor wear was represented mathematically in a sinusoidal form. The wheel assembly vibration from the brake torque variation is transmitted to driver interface points such as the seat track and the steering wheel. The steering wheel lateral acceleration at the 12 o’clock position, driver seat acceleration, and spindle fore-aft acceleration were reviewed to explore the physics of brake pulsation. It was found that the phase angle between the left and right brake torque generated a huge variation in brake pulsation performance.

Human driving behavior's inherent variability, randomness, individual differences, and dynamic vehicle-road situations give human-machine cooperative (HMC) driving considerable uncertainty, which affects the applicability and effectiveness of HMC control in complex scenes. To overcome this challenge, we present a novel data-enabled game output regulation approach for HMC driving. Firstly, a global human-vehicle-road (HVR) model is established considering the varied driver's steering characteristic parameters, such as delay time, preview time, and steering gain, as well as the uncertainty of tire cornering stiffness and variable road curvature disturbance. The robust output regulation theory has been employed to ensure the global DVR system's closed-loop stability, asymptotic tracking, and disturbance rejection, even with an unknown driver's internal state. Secondly, an interactive shared steering controller has been designed to provide personalized driving assistance.

A redundant system refers to a system that operates identical unit systems simultaneously to enhance robustness to fault. In particular, considering system complexity, a redundant system consisting of two identical unit systems is widely used. However, dual-system redundancy can detect the presence of malfunction when the outputs of the two unit systems differ, but it is challenging to identify the normally functioning unit system. Therefore, the functionality can degrade or be interrupted even when a normally operating unit system is present. Hence, research is actively ongoing to address the challenge of identifying the normally functioning unit system. This study proposes an algorithm to identify the normally operating sensor in the event of a steering angle sensor fault in a redundant Electronic Power Steering (EPS) system. In this paper, an Extended Kalman Filter is designed based on the Bicycle model of vehicle dynamics to estimate the steering angle of the steering wheel.

Compared with urban areas, the road surface in mountainous areas generally has a larger slope, larger curvature and narrower width, and the vehicle may roll over and other dangers on such a road. In the case of limited driver information, if the two cars on the mountain road approach fast, it is very likely to occur road blockage or even collision. Multi-vehicle cooperative control technology can integrate the driving data of nearby vehicles, expand the perception range of vehicles, assist driving through multi-objective optimization algorithm, and improve the driving safety and traffic system reliability. Most existing studies on cooperative control of multiple vehicles is mainly focused on urban areas with stable environment, while ignoring complex conditions in mountainous areas and the influence of driver status. In this study, a digital twin based multi-vehicle cooperative warning system was proposed to improve the safety of multiple vehicles on mountain roads.

The tire cornering stiffness plays a vital role in the functionality of vehicle dynamics control systems, particularly when it comes to stability and path tracking controllers. This parameter relies on various external variables such as the tire/ambient temperature, tire wear condition, the road surface state, etc. Ensuring a reliable estimation of the cornering stiffness value is crucial for control systems. This ensures that these systems can accurately compute actuator requests in a wide range of driving conditions. In this paper, a novel estimation method is introduced that relies solely on standard vehicle sensor data, including data such as steering wheel angles, longitudinal acceleration, lateral acceleration, yaw rate, and vehicle speed, among others. Initially, the vehicle's handling characteristics are deduced by estimating the understeer gradient.

The flight area of drones and other unmanned aerial vehicles (UAVs) had been highly restricted but has been relaxing, including flights beyond the scope of sight. Deregulation without aircraft-reliability improvement increases the risk of accidents. However, demanding high reliability for all aircraft leads to an increase in the price of the aircraft. Therefore, if airspace restrictions are relaxed for more reliable aircraft, the cost of higher reliability and its benefits can be balanced. This will improve efficiency and optimize cost-effectiveness. The purpose of this proposal is to balance the cost of aircraft-reliability improvement (which allows flight to continue in the event of a failure) and its advantages. Specifically, the author proposes rules that apply more relaxed airspace restrictions to UAVs with higher FCLs (Flight Continuity Possibility Levels) and stricter airspace restrictions to those with lower FCLs.

The focus on driver and occupant safety as well as comfort is increasing rapidly while designing commercial vehicles in India. Improvements in the road network have enhanced road transport for commercial vehicles. Apart from the cost of operation and fuel economy, the commercial vehicles must deliver goods within stipulated time. These factors resulted in higher speed of operation for commercial vehicles. The design should not compromise the safety of the vehicle at these higher speeds of operation. The vehicle should obey the driver’s intended direction at all speeds and the response of the vehicle to driver input must be predictable without much larger surprises which can lead to accidents. The commercial vehicles are designed with rigid axle and RCB type steering system. This suspension and steering design combination introduce steering errors when vehicle travel over bump, braked and while cornering.

Heavy Commercial Road Vehicles (HCRVs) may be more susceptible to rollover incidents due to their higher centre of gravity position than passenger vehicles, and rollover is one of the significant causes of HCRV accidents. Therefore, variation in vehicle roll behaviour becomes crucial to the safety of an HCRV. Toe misalignment is a commonly observed phenomenon in HCRVs, and studying its impact on roll behaviour is important. In this study, the impact of the symmetric toe and thrust misalignment on the roll behaviour of an HCRV is analysed using IPG TruckMaker®, a vehicle dynamics simulation software. A ramp steer manoeuvre was used for the simulations, and the toe misalignment on a wheel was chosen from the range [-0.21°, 0.21°]. Variation in roll behaviour was quantified using the steering wheel angle at which one-wheel lift-off (OWL) occurred (SWAL).

Automated parking systems for cars have become the need of the hour globally, gaining wide acceptance from customers, and hence OEMs are working towards achieving precise/accurate automated parking. Various algorithms are being developed to plan the trajectory of the vehicle to be moved in/out of the desired parking slot. Most of these algorithms assume a static environment and don’t account for highly dynamic objects. Accounting for such objects is vital especially when autonomously exiting a parking slot and merging with traffic. This paper summarizes our initial efforts in addressing dynamic objects, specifically the ‘right of way’ aspects, while autonomously exiting a parking slot. In this study, we propose a novel approach for generating linear and angular velocity profiles using Deep Reinforcement Learning (DRL) in conjunction with Hybrid A* path planning for autonomous vehicles (AVs) navigating parking maneuvers.

Virtual simulation is a fundamental tool for the development of new vehicles, both for individual components and for complete subsystems and full vehicles. Many software tools exist in the automotive sector to assess full-vehicle behavior and performance, including multibody software and algorithms based on 14 (or more) degrees-of-freedom vehicle dynamics models. In order to reproduce the testing maneuvers and typical vehicle mission, a key part of such simulation tools is the virtual driver algorithm. It is essential to implement a control logic that reproduces the handling response of the driver, so that the closed-loop maneuvers can be evaluated. However, the response of typical virtual drivers is not always similar to the human driving characteristics. Virtual driver algorithms can perform very fast, precise, and smooth steering and pedal actions, while humans display a more variable, delayed and often not optimal actions.

This paper focuses on lane-changing trajectory planning and trajectory tracking control in autonomous vehicle technology. Aiming at the lane-changing behavior of autonomous vehicles, this paper proposes a new lane-changing trajectory planning method based on particle swarm optimization (PSO) improved third-order Bezier curve path planning and polynomial curve speed planning. The position of Bezier curve control points is optimized by the particle swarm optimization algorithm, and the lane-changing trajectory is optimized to improve the comfort of lane changing process. Under the constraints of no-collision and vehicle dynamics, the proposed method can ensure that the optimal lane-changing trajectory can be found in different lane-changing scenarios. To verify the feasibility of the above planning algorithm, this paper designs the lateral and longitudinal controllers for trajectory tracking control based on the vehicle dynamic tracking error model.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

Unmanned Aerial Vehicles (UAVs) encounter various uncertainties, including unfamiliar environments, signal delays, limited control precision, and other disturbances during task execution. Such factors can significantly compromise flight safety in complex scenarios. In this paper, to enhance the safety of UAVs amidst these uncertainties, a control accuracy prediction model based on ensemble learning abnormal state detection is designed. By analyzing the historical state data, the trained model can be used to judge the current state and obtain the command tracking control accuracy of the UAV at that instant. Ensemble learning offers superior classification capabilities compared to weak learners, particularly for anomaly detection in flight data. The learning efficacy of support vector machine, random forest classifier is compared and achieving a peak accuracy of 95% for the prediction results using random forest combined with adaboost model .

Conventional trajectory planning methods encounter various challenges: Inability to better distinguish different types of vehicles, and failure to consider the difference between perceived threats or risks during asymmetric and symmetric interactions for autonomous vehicles. To solve these issues, the insufficiency of the traditional risk-field model is analyzed, and an asymmetric aggressiveness model is investigated in this study, which quantifies the suffered aggressiveness of vehicles. Then, the asymmetric aggressiveness model and the static potential risk field describing the road structure are used as the control objectives of the optimal controller to avoid collisions. Furthermore, a three-degree-of-freedom vehicle dynamics model is constructed, and the optimal feasible trajectory is planned by using the model predictive control algorithm.

This document provides recommended practices for the design, development, and verification testing of NWS systems.

This SAE Recommended Practice defines key terms used in the description and analysis of video based driver eye glance behavior, as well as guidance in the analysis of that data. The information provided in this practiced is intended to provide consistency for terms, definitions, and analysis techniques. This practice is to be used in laboratory, driving simulator, and on-road evaluations of how people drive, with particular emphasis on evaluating Driver Vehicle Interfaces (DVIs; e.g., in-vehicle multimedia systems, controls and displays). In terms of how such data are reduced, this version only concerns manual video-based techniques. However, even in its current form, the practice should be useful for describing the performance of automated sensors (eye trackers) and automated reduction (computer vision).

Battery electric vehicle (BEV) adoption and complex powertrains pose new challenges to automotive industries, requiring comprehensive testing and validation strategies for reliability and safety. Hardware-in-the-loop (HIL) based real-time simulation is important, with cooperative simulation (co-simulation) being an effective way to verify system functionality across domains. Fault injection testing (FIT) is crucial for standards like ISO 26262. This study proposes a HIL-based real-time co-simulation environment that enables fault injection tests in BEVs to allow evaluation of their effects on the safety of the vehicle. A Typhoon HIL system is used in combination with the IPG CarMaker environment. A four-wheel drive BEV model is built, considering high-fidelity electrical models of the powertrain components (inverter, electric machine, traction battery) and the battery management system (BMS).