Search Results

Abstract Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see” the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus, it is imperative to test the vehicle extensively in all conditions which it may experience. However, the development of robust AV subsystems requires repeatable, controlled testing—while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world being developed.

Abstract This article investigates the dynamics of non-smooth and nonlinear oscillations of a bicycle-car model, considering the tire-road separation. Road contact applies a non-holonomic constrain on the dynamics system that makes the equations of motion to be different under in-contact and off-contact conditions. The set of nonlinear equations of the system has been formulated based on nondimensionalization to minimize the number of parameters and generalize the results. To compare the quality of different suspensions in reducing the unpleasant no-contact conditions, we define a contact-free fraction indicator to measure the separation fraction time during a cycle of steady-state oscillation. An observation of frequency responses including vertical displacements, the pitch mode, and the domain of contact-free fraction of time has been investigated to clarify engineering design directions.

Abstract When the vehicle is under braking condition in the longitudinal motion, the vehicle body will tilt due to the inertial force in motion. A high amplitude will result in uncomfortable feelings of the occupant, such as nervousness or dizziness. To solve the problem, this article presents an adaptive damping system (ADS), which combines the vehicle anti-pitch compensation control with the mixed skyhook (SH) and acceleration-driven-damper (ADD) control algorithm. This ADS can not only improve the vibration effect of the vertical motion for the vehicle but also consider the longitudinal motion of the vehicle body. In addition, a new damper mechanical hardware-in-the-loop test bench is built to verify the effectiveness of the algorithm.

Abstract The ground-effect problems of loss of thrust and fountain-effect instabilities are quantified. Experiments to control and augment ground-effect lift and stability are presented, including jet momentum reflection and fountain redirection using various types of internal and external underbody ventral strakes. By strategically designing the vertical takeoff and landing (VTOL) ventral surface, reflection of the impinging fountain momentum is possible so that instead of losing 10% thrust while in ground-effect, remarkably, thrust is augmented 10% or more to a considerable height above the ground, in addition to stabilizing random pitch and roll moments caused by fountain instability.

Abstract A critical component of vehicle dynamic control systems is the accurate and real-time knowledge of the vehicle’s key states and parameters when running on the road. Such knowledge is also essential for vehicle closed-loop feedback control. Vehicle state and parameter estimation has gradually become an important way to soft-sense some variables that are difficult to measure directly using general sensors. In this work, a seven degrees-of-freedom (7-DOF) nonlinear vehicle dynamics model is established, where consideration of the Magic formula tire model allows us to estimate several vehicle key states using a hybrid algorithm containing an unscented Kalman filter (UKF) and a genetic algorithm (GA). An estimator based on the hybrid algorithm is compared with an estimator based on just a UKF. The results show that the proposed estimator has higher accuracy and fewer computation requirements than the UKF estimator.

Abstract In this article, a hierarchical coordinated control algorithm for integrating active rear steering and driving/braking force distribution (ARS+D/BFD) was presented. The upper-level control was synthesized to generate the required rear steering angle and external yaw moment by using a sliding-mode controller. In the lower-level controller, a control allocation algorithm considering driving/braking actuators and tire forces constraints was designed to assign the desired yaw moment to the four wheels. To this end, an optimization problem including several equality and inequality constraints were defined and solved analytically. Finally, computer simulation results suggest that the proposed hierarchical control scheme was able to help to achieve substantial enhancements in handling performance and stability.

Abstract Advanced passenger vehicles are complex dynamic systems that are equipped with several actuators, possibly including differential braking, active steering, and semi-active or active suspensions. The simultaneous use of several actuators for integrated vehicle motion control has been a topic of great interest in literature. To facilitate this, a technique known as control allocation (CA) has been employed. CA is a technique that enables the coordination of various actuators of a system. One of the main challenges in the study of CA has been the representation of actuator dynamics in the optimal CA problem (OCAP). Using model predictive control allocation (MPCA), this problem has been addressed. Furthermore, the actual dynamics of actuators may vary over the lifespan of the system due to factors such as wear, lack of maintenance, etc. Therefore, it is further required to compensate for any mismatches between the actual actuator parameters and those used in the OCAP.

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract Sustainable practices are taking precedence across many industries, as evident from their shift towards the use of environmentally responsible materials, such as natural fiber-reinforced acrylated epoxidized soybean oil (NF-AESO). However, due to the lower reactivity of AESO, the curing reaction usually requires higher temperatures and longer curing time (e.g., 150°C for 6-12 h), thus making the entire process unsustainable. In this study, we demonstrate the potential power of photons towards manufacturing NF-AESO composites in a sustainable manner at room temperature (RT) within 10 min. Two photoinitiators, i.e., the 2,2-dimethoxy phenylacetophenone (DMPA) and 1-hydroxycyclohexyl phenyl ketone (HCPK), were evaluated and compared with the thermal initiator, i.e., tert-butyl perbenzoate (TBPB). Based on the mechanical performance of the AESOs, the photoinitiation system for NF-AESO was optimized.

Abstract The external driving environment of an autonomous driving vehicle is complex and changeable. In this article, the trajectory tracking control with obstacle avoidance based on model predictive control was presented. Specifically, double-level control scheme by controlling the front steering angle was used in our research, and the double level is composed of the high level of model predictive controller for local trajectory planning and low level of model predictive controller for trajectory tracking. At high level, the local trajectory planner based on the point-mass model was designed. Then, at low level, the linear time-varying vehicle dynamics model was presented, and the trajectory tracking controller was proposed considering control variable, control increment, and output constraint. Finally, the trajectory tracking performance was tested in co-simulation environment with CarSim and Simulink, and the tracking errors were analyzed.

Abstract The promise and potential for a future of automated vehicles (AVs) remains great, with safety and societal transformations that may rival the original introduction of the automobile. Yet an inability for industry and governments to define what it means for an AV to drive safely has tempered enthusiasm and risks causing a “winter of AV” just like the one that affected Artificial Intelligence technologies decades ago, which is only now being overcome. Towards this end, the Responsibility-Sensitive Safety (RSS) model was introduced as an open and transparent white-box, an interpretable and scalable formal model that defines minimum safety requirements based on reasonable assumptions of others, balancing safety and usefulness for automated driving vehicles.

Abstract This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results.

Abstract Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated.

Abstract Non-pneumatic tires (NPTs) have been widely used due to their advantages of no occurrence of puncture-related problems, no need of air maintenance, low rolling resistance, and improvement of passenger comfort due to its better shock absorption. It has a variety of applications as in earthmovers, planetary rover, stair-climbing vehicles, and the like. Recently, the unique puncture-proof tire system (UPTIS) NPT has been introduced for passenger vehicles segment. The spoke design of NPT-UPTIS has a significant effect on the overall working performance of tire. Optimized tire performance is a crucial factor for consumers and original equipment manufacturers (OEMs). Hence to optimize the spoke design of NPT-UPTIS spoke, the top and bottom curve of spoke profile have been described in the form of analytical equations. A generative design concept has been introduced to create around 50,000 spoke profiles.

Abstract This article introduces a finite element (FE) approach to determine tire deformation and its effect on open-wheel race car aerodynamics at high vehicle velocities. In recent literature tire deformation was measured optically. Combined loads like accelerating at a corner exit are difficult to reproduce in wind tunnels and require several optical devices to measure the tire deformation. In contrast, an FE approach is capable of determining the tire deformation in combined load states accurately. Additionally, the temperature influence on tire deformation is investigated. The FE tire model was validated using three-dimensional (3D) scan measurements; stiffness measurements in the vertical, lateral, and longitudinal direction; and the change of loaded radius with speed at different loads, respectively. The deformed shape of the tire of the FE model was used in a computational fluid dynamics (CFD) simulation.

Abstract To investigate the effect of a tire blowout (TBO) on the dynamics of the vehicle comprehensively, a three-dimensional full-vehicle multibody mathematical model is developed and integrated with the nonlinear Dugoff’s tire model. In order to ensure the validity of the developed model, a series of standard maneuvers is carried out and the resulting response is verified using the high-fidelity MSC Adams package. Consequently, the in-plane, as well as out-of-plane dynamics of the vehicle, is extensively examined through a sequence of TBO scenarios with various blown tires and during both rectilinear and curvilinear motion. Moreover, the different possible inputs from the driver, the road bank angle, and the antiroll bar have been accounted for. The results show that the dynamic behavior of the vehicle is tremendously affected both in-plane and out-of-plane and its directional stability is degraded.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract A novel geometry for a six degrees of freedom (6DOF) unmanned aerial vehicle (UAV) rotary wing aircraft is introduced and a flight mechanical analysis is conducted for an aircraft built in accordance to the thrust vectors of the proposed geometry. Furthermore, the necessary mathematical operations and control schemes are derived to fly an aircraft with the proposed geometry. A system identification of the used propulsion system with the necessary thrust reversal in the form of bidirectional motors and propellers was conducted at a whirl tower. The design of the first prototype aircraft is presented as well as the first flight test results. It could be demonstrated that an aircraft with the thrust vectors oriented according to the proposed geometry works sufficiently and offers unique maneuvering capabilities that cannot be reached with a conventional design.

Abstract Pyrotechnic seat belt pretensioners typically remove 8–15 cm of belt slack and help couple an occupant to the seat. Our study investigated pretensioner deployment on forward-leaning, live volunteers. The forward-leaning position was chosen because research indicates that passengers frequently depart from a standard sitting position. Characteristics of the 3D kinematics of forward-leaning volunteers following pretensioner deployment determines if body size is correlated with subject response. Nine adult subjects (three female), ages 18–43 years old, across a wide range of body sizes (50–120 kg) were tested. The age was limited to young, active adults as pyrotechnic pretensioners can deliver a notable force to the trunk. Subjects assumed a forward-leaning position, with 26 cm between C7 and the headrest, in a laboratory setting that replicated the passenger seat of a vehicle.

Abstract Equal-channel angular pressing (ECAP) is among the most applicable severe plastic deformation processes used to fabricate ultrafine-grained materials with superior mechanical properties. In this work, a commercial purity aluminum has been processed via ECAP process up to four passes. The influence of ECAP routes (A and Bc) on the mechanical properties of the material and its grain size was investigated. Microstructural observations of the as-annealed and the rods processed via ECAP were undertaken using optical microscopy. Hardness profiles and contour maps of sections cut perpendicularly and parallel to the load direction were assessed to investigate the effect of ECAP processing on the hardness distribution across the deformed rods. Compressive properties of the rods were also examined. In addition, digital images correlation was used to display the stress distribution along the longitudinal section of the processed sample during the compression test.