Search Results

Abstract With the rapid development and the growing deployment of autonomous ground vehicles (AGVs) worldwide, there is an increasing need to design reliable, efficient, robust, and scalable motion-planning algorithms. These algorithms are crucial for fulfilling the desired goals of safety, comfort, efficiency, and accessibility. To design optimal motion-planning algorithms, it is beneficial to explore existing techniques and make improvements by addressing the limitations of associated techniques, utilizing hybrid algorithms, or developing novel strategies. This article categorizes and overviews numerous motion-planning algorithms for AGVs, shedding light on their strengths and weaknesses for a comprehensive understanding.

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.

Abstract The ground vibration test (GVT) is an important phase in a new aircraft development program, or the structural modification of a certified aircraft, to experimentally determine the structural vibrational modes of the aircraft and their modal parameters. These modal parameters are used to validate and correlate the dynamic finite element model of the aircraft to predict potential structural instabilities (such as flutter), assessing the significance of modifications to research vehicles by comparing the modal data before and after the modification and helping to resolve in-flight anomalies. Due to the high cost and the extensive preparations of such tests, a new method of vibration testing called the taxi vibration test (TVT) rooted in operational modal analysis (OMA) was recently proposed and investigated as an alternative method to conventional GVT.

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Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract An automatic collision avoidance control method integrating optimal four-wheel steering (4WS) and direct yaw-moment control (DYC) for autonomous vehicles on curved road is proposed in this study. Optimal four-wheel steering is used to track a predetermined trajectory, and DYC is adopted for vehicle stability. Two single lane change collision avoidance scenarios, i.e., a stationary obstacle in front and a moving obstacle at a lower speed in the same lane, are constructed to verify the proposed control method. The main contributions of this article include (1) a quintic polynomial lane change trajectory for collision avoidance on curved road is proposed and (2) four different kinds of control method for autonomous collision avoidance, namely 2WS, 2WS+DYC, 4WS, and 4WS+DYC, are compared. In the design of DYC controller, two different feedback control methods are adopted for comparison, i.e., sideslip angle feedback and yaw rate feedback.

Abstract Real-world driving in diverse traffic must cope with dynamic environments including a multitude of agents with uncertain behaviors. This poses a challenging motion planning and decision-making problem, as suitable algorithms should manage to obtain optimal solutions considering nearby vehicles. The state-of-the-art way of environment and action generalization is built on mathematical modeling and probabilistic programming of idealistic incidents. In this article we present dynamic anytime decision making, a decision-making algorithm that takes advantage of natural evolutionary and developmental processes to make decisions for an autonomous vehicle navigating in traffic. The methodology to achieve multidimensional judgment under unforeseen circumstances is to enable stochastic Bayesian game theory when modeling interactive properties and scenario estimation.

Abstract A unique torque converter test setup was used to measure the torque transmissibility frequency response function of four torque converter clutch dampers using a stepped, multi-sine-tone, excitation technique. The four torque converter clutch dampers were modeled using a lumped parameter technique, and the damper parameters of stiffness, damping, and friction were estimated using a manual, iterative parameter estimation process. The final damper parameters were selected such that the natural frequency and damping ratio of the simulated torque transmissibility frequency response functions were within 10% and 20% error, respectively, of the experimental modal parameters. This target was achieved for all but one of the tested dampers. The damper models include stiffness nonlinearities, and a speed-dependent friction torque due to centrifugal loading of the damper springs.

Abstract Accurate estimation of traction force is essential for the development of advanced control systems, particularly in the domain of autonomous driving. This study presents an innovative approach to enhance the estimation of tire–road interaction forces under combined slip conditions, employing a combination of empirical models and neural networks. Initially, the well-known Pacejka formula, or magic formula, was adopted to estimate tire–road interaction forces under pure longitudinal slip conditions. However, it was observed that this formula yielded unsatisfactory results under non-pure slip conditions, such as during curves. To address this challenge, a neural network architecture was developed to predict the estimation error associated with the Pacejka formula. Two distinct neural networks were developed. The first neural network employed, as inputs, both longitudinal slip ratios of the driving wheels and the slip angles of the driving wheels.

Abstract To address the challenge of directly measuring essential dynamic parameters of vehicles, this article introduces a multi-source information fusion estimation method. Using the intelligent front camera (IFC) sensor to analyze lane line polynomial information and a kinematic model, the vehicle’s lateral velocity and sideslip angle can be determined without extra sensor expenses. After evaluating the strengths and weaknesses of the two aforementioned lateral velocity estimation techniques, a fusion estimation approach for lateral velocity is proposed. This approach extracts the vehicle’s lateral dynamic characteristics to calculate the fusion allocation coefficient. Subsequently, the outcomes from the two lateral velocity estimation techniques are merged, ensuring rapid convergence under steady-state conditions and precise tracking in dynamic scenarios.

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Abstract Ground vibration testing (GVT) is an important phase of the development, or the structural modification of an aircraft program. The modes of vibration and their associated parameters extracted from the GVT are used to modify the structural model of the aircraft to make more reliable dynamics predictions to satisfy certification authorities. Due to the high cost and the extensive preparations for such tests, a new method of vibration testing called taxi vibration testing (TVT) rooted in operational modal analysis (OMA) was recently proposed and investigated by the German Institute for Aerospace Research (DLR) as alternative to conventional GVT. In this investigation, a computational framework based on fully coupled flexible multibody dynamics for TVT is presented to further investigate the applicability of the TVT to flexible airframes. The time domain decomposition (TDD) method for OMA was used to postprocess the response of the airframe during a TVT.

Abstract Lateral control is an essential part of driverless mining truck systems. However, the considerable steering lag and poor tracking accuracy limit the development of unmanned mining. In this article, a dynamic preview distance was designed to resist the steering lag. Then the vehicle–road states, which described the real-time lateral and heading errors between the vehicle and the target road, was defined to describe the control strategy more efficiently. In order to trade off the tracking accuracy and stability, the Takagi–Sugeno (TS) fuzzy method was used to adjust the weight matrix of the linear quadratic regulator (LQR) for different vehicle–road states. Based on the actual mine production environment and the TR100 mining truck, experimental results show that the TS-LQR algorithm performed much better than the pure pursuit algorithm.

Abstract Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity for vehicles to perform autonomous longitudinal control to navigate safely and efficiently through sequences of V2I-enabled intersections, known as connected corridors. Existing research has proposed several control systems to navigate these corridors while minimizing energy consumption and travel time. This article analyzes and compares the simulated performance of three different autonomous navigation systems in connected corridors: a V2I-informed constant acceleration kinematic controller (V2I-K), a V2I-informed model predictive controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent. A rules-based controller that does not use V2I information is implemented to simulate a human driver and is used as a baseline. The performance metrics analyzed are net energy consumption, travel time, and root-mean-square (RMS) acceleration.

Abstract The United Nation Economic Commission for Europe (UNECE) Regulation 155—Cybersecurity and Cybersecurity Management System (UN R155) mandates the development of cybersecurity management systems (CSMS) as part of a vehicle’s lifecycle. An inherent component of the CSMS is cybersecurity risk management and assessment. Validation and verification testing is a key activity for measuring the effectiveness of risk management, and it is mandated by UN R155 for type approval. Due to the focus of R155 and its suggested implementation guideline, ISO/SAE 21434:2021—Road Vehicle Cybersecurity Engineering, mainly centering on the alignment of cybersecurity risk management to the vehicle development lifecycle, there is a gap in knowledge of proscribed activities for validation and verification testing.

Abstract In this article, we present a spatiotemporal trajectory planning algorithm for emergency obstacle avoidance. Utilizing obstacle and driving environment data from the sensing module, we construct a 3D spatiotemporal grid map. This informs our improved hybrid A* algorithm, which identifies collision-safe, dynamically feasible trajectories. The traditional hybrid A* algorithm is enhanced in three significant ways to make the search practical and feasible: (1) optimizing search efficiency with motion primitives based on child node acceleration, (2) integrating collision risk into the heuristic function to reduce ineffective node exploration, and (3) introducing a One-Shot search based on the Optimal Boundary Value Problem (OBVP) to improve goal state searches. Finally, the algorithm is tested in two scenarios: (1) a vehicle cut-in from an adjacent lane and (2) a pedestrian crossing.

Abstract This study investigates the use of a road weather model (RWM) as a virtual sensing technique to assist autonomous vehicles (AVs) in driving safely, even in challenging winter weather conditions. In particular, we investigate how the AVs can remain within their operational design domain (ODD) for a greater duration and minimize unnecessary exits. As the road surface temperature (RST) is one of the most critical variables for driving safety in winter weather, we explore the use of the vehicle’s air temperature (AT) sensor as an indicator of RST. Data from both Road Weather Information System (RWIS) stations and vehicles measuring AT and road conditions were used. Results showed that using only the AT sensor as an indicator of RST could result in a high number of false warnings, but the accuracy improved significantly with the use of an RWM to model the RST.

Abstract Since the complexity of modern vehicles is increasing continuously, car manufacturers are forced to improve the efficiency of their development process to remain profitable. A frequently mentioned measure is the consequent integration of virtual methods. In this regard, objective evaluation criteria are essential for the virtual design of driving dynamics. Therefore, this article aims to identify robust objective evaluation criteria for the nonlinear combined longitudinal and lateral dynamics of a vehicle. The article focuses on the acceleration in a turn maneuver since available objective criteria do not consider all relevant characteristics of vehicle dynamics. For the identification of the objective criteria, a generic method is developed and applied. First, an open-loop test procedure and a set of potential robust objective criteria are defined.

Abstract The uncertainty of a driver’s state, the variability of the traffic environment, and the complexity of road conditions have made driving behavior a critical factor affecting traffic safety. Accurate predicting of driving behavior is therefore crucial for ensuring safe driving. In this research, an efficient framework, distilled routing transformer (DRTR), is proposed for driving behavior prediction using multiple modality data, i.e., front view video frames and vehicle signals. First, a cross-modal attention distiller is introduced, which distills the cross-modal attention knowledge of a fusion-encoder transformer to guide the training of our DRTR and learn deep interactions between different modalities. Second, since the multi-modal learning usually requires information from the macro view to the micro view, a self-attention (SA)-routing module is custom-designed for SA layers in DRTR for dynamic scheduling of global and local attentions for each input instance.

Abstract In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist.