On-board Vehicle Sensors Research Articles

Estimating vehicle sideslip angle through kinematic and dynamic contributions: Theory and experimental results

Vehicle lateral stability plays an important role within vehicle passenger safety. The study of lateral stability is typically related to investigating the dynamics of relevant vehicle states: among these, the vehicle sideslip angle ([Formula: see text]) emerges as a prominent candidate. Sideslip angle measurement is expensive and impractical, hence estimation techniques are often used, typically based on Kalman filters or neural networks, both with their issues. This work presents an alternative estimation method based on the idea of splitting sideslip angle into kinematic and dynamic contributions, and by observing that the kinematic contribution is straightforward to estimate. Therefore, efforts are devoted into estimating dynamic sideslip angle, which is herein obtained through a parametric interpolation harnessing lateral acceleration. Only data available from traditional vehicle onboard sensors are used in the process. Experimental results are presented along several manoeuvres on a full-scale vehicle, with the estimator running online within a dSPACE unit, ultimately supporting the efficacy and real-time feasibility of the proposed approach.

Smart Bridge Damage Assessment through Integrated Multi-Sensor Fusion Vehicle Monitoring

Abstract. This study explores the efficacy of vehicle-assisted monitoring for bridge damage assessment, emphasizing the integration of diverse sensor data sources. A novel method utilizing a deep neural network is proposed, enabling the fusion of fixed sensors on bridges and onboard vehicle sensors for damage assessment. The network offers scalability, robustness, and implementability, accommodating various measurement types while handling noise and dynamic loading conditions. The main novel aspect of our work is its ability to extract damage-sensitive features without signal preprocessing for future bridge health monitoring systems. Through numerical evaluations, considering realistic operational conditions, the proposed method demonstrates the capability to detect subtle damage under varying traffic conditions. Findings underscore the importance of integrating vehicle and bridge sensor data for reliable damage assessment, recommending strategies for optimal monitoring implementation by road authorities and bridge owners.

The Unscented Kalman Filter for Nonlinear Parameter Identification of Adaptive Cruise Control Systems

This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) systems. Although the core functionality of stock ACC systems, including their proprietary control logic and parameters, is not publicly available, this work considers a car-following scenario with a human-driven vehicle (leader) and an ACC engaged ego vehicle (follower) that employs a constant time-headway policy (CTHP). The objective of the DUKF is to determine the CTHP parameters of the ACC by using real-time observations of space-gap and relative velocity from the vehicle's onboard sensors. Real-time parameter identification of stock ACC systems is essential for assessing their string stability, large-scale deployment on motorways, and impact on traffic flow and throughput. In this regard, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_{2}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_\infty$</tex-math></inline-formula> string stability conditions are considered. The observability rank condition for nonlinear systems is adopted to evaluate the ability of the proposed estimation scheme to estimate stock ACC system parameters using empirical data. The proposed filter is evaluated using empirical data collected from the onboard sensors of two 2019 SUV vehicles, namely Hyundai Nexo and SsangYong Rexton, equipped with stock ACC systems; and is compared with batch and recursive least-squares optimization. The set of ACC model parameters obtained from the proposed filter revealed that the commercially implemented ACC system of the considered vehicle (Hyundai Nexo) is neither <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_{2}$</tex-math></inline-formula> nor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_\infty$</tex-math></inline-formula> string stable.

A FRAMEWORK FOR MULTI-SENSOR POSITIONING AND MAPPING FOR AUTONOMOUS VEHICLES

Abstract. Autonomous vehicles (AVs) are cars, buses or trucks that can travel with limited or no human intervention. They combine sensors, perception systems, and software to control, navigate, and safely drive the vehicle. AVs promise to improve the efficiency of our transportation system, reduce collisions and traffic congestion and improve mobility for our growing population. As the world continues to move toward a more efficient transportation system driven by AVs, there is a growing demand for new technologies that guarantee efficient and safe operation. Trust in AVs hinges on the reliability of autonomy, including the crucial task of positioning, which should be accurately provided at high precision everywhere for all environments. This paper introduces a framework for developing and deploying robust positioning and mapping systems for AVs at a submeter level of accuracy with high integrity. We introduce a new paradigm for the positioning and mapping of AVs that expands the capabilities of the present technologies and enables the processing of a broader range of complementary sensors and systems on a single platform. The target is to sustain this performance seamlessly everywhere in all operating and weather conditions relying on the suite of wireless and perception systems in addition to the vehicle's onboard sensors. Some sample results demonstrating the submeter level of positioning in various environments are discussed in this paper. This research will significantly impact the acceptance of and trust in AVs by enhancing safety and reliability and decreasing the failure rate in degraded environments.

Anomaly detection and string stability analysis in connected automated vehicular platoons

In this study, we develop a comprehensive framework to model the impact of cyberattacks on safety, security, and head-to-tail stability of connected and automated vehicular platoons. First, we propose a general platoon dynamics model with heterogeneous time delays that may originate from the communication channel and/or vehicle onboard sensors. Based on the proposed dynamics model, we develop an augmented state extended Kalman filter (ASEKF) to smooth sensor readings, and use it in conjunction with an anomaly detector to detect sensor anomalies. Specifically, we consider two detectors: a parametric detector, the χ2-detector, and a learning-based detector, the one class support vector machine (OCSVM). We investigate the detection power of all combinations of vehicle dynamics models (EKF and ASEKF) and detectors (χ2 and OCSVM). Furthermore, we introduce a novel concept in string stability, namely, pseudo string stability, to measure a platoon’s string stability under cyberattacks and model uncertainties. We demonstrate the relationship between the pseudo string stability of a platoon and its detection rate, which enables us to identify the critical detection sensitivity/recall that the platoon’s members should meet for the platoon to remain pseudo string stable.

Minimum Time Headway in Platooning Systems Under the MPF Topology for Different Wireless Communication Scenario

The multiple-predecessor following (MPF) topology is used in vehicle platoons to make it robustly string stable and reduce the minimum employable time headway. It has been demonstrated that communication imperfections such as time delays coming from wireless communications can affect string stability as well as the minimum time headway required to guarantee string stability. Specifically, it was shown that the larger the time delay, the longer the minimum time headway will be. However, by utilizing on-board vehicle sensors, such as radar, lidar and cameras, the distance and speed of nearby vehicles can be measured almost instantaneously, i.e., with almost no delay. Another effective parameter on string stability and minimum time headway is the heterogeneity of the vehicles. Due to the immense complexity of the MPF topology, string stability analysis of this topology in literature has been confined to homogeneous platoons. In this paper, we consider the case of heterogeneous platoons under the MPF topology with the use of the combination of sensors and wireless communications for receiving information. Following that, we find conditions to guarantee the internal and string stability for the heterogeneous case and propose the minimum time headway required to guarantee string stability. Finally, we provide a table, in which we propose the minimum time headway for two other wireless communication scenarios as well: (i) having no communication delay and (ii) having fully-delayed information, i.e., all information, whether it comes from the ego vehicle or its predecessors, is delayed. In addition to exploring the analysis of string stability for the vehicles with more possible connections (vehicles after the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r^{th}$ </tex-math></inline-formula> vehicle, when information from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> immediate vehicles is used), we study the string stability conditions (with which we aim at avoiding collisions) and find the minimum time headway for the first few vehicles (vehicle <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> and all its predecessors). Numerical results clearly show the effectiveness of the proposed lower bounds.

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.

Pavement Crack Monitoring: Literature Review

Crack monitoring of pavements is an ever-evolving technology with new crack identification technologies being introduced frequently. Although older technologies consisted of physical removing the pavement section using coring, however new methods are available that are non-destructive and yield a higher performance than conventional technologies. This paper compiles various crack monitoring technologies such as wireless sensor networks, photo imaging, laser imaging, 3D road surface profile scans, acoustics wave propagation technology, embedded strain sensors and onboard vehicle sensors that majorly use an artificial intelligence algorithm to identify and categorize the cracks. The research also includes the use of convolutional neural network that can be used to analyze pavement images and such neural network can localize and classify the cracks for crack initiation and propagation stage. The research concludes with the favor of using the optical imaging technology called Syncrack which serves better performance in terms of time of prediction by 25% and accuracy by 30% when compared to other sensing technologies.

Vehicle assisted bridge damage assessment using probabilistic deep learning

Vehicle assisted monitoring has shown promising potential for the condition assessment of existing bridges in a road network, by removing practical complications faced in traditional Structural health monitoring (SHM) methods such as traffic interruption and dense deployment of sensors. However, the combination of different measurement sources during vehicle assisted monitoring has not yet been fully explored. This paper aims to evaluate the potential benefit of considering multiple measured responses from various sources, including fixed sensors on the bridge and on-board vehicle sensors. To this end, this paper proposes a Probabilistic Deep Neural Network, a stochastic data-driven framework for damage assessment. This framework enables the combination of vehicle and bridge responses to extract damage sensitive features for the classification of different damage states. In addition, the proposed method estimates the uncertainty of its predictions, providing an indication of the reliability of the result. The proposed method is validated using two numerical based case studies while considering realistic operational conditions, which include temperature oscillations, additional traffic, and measurement noise. The results from this study indicate that combining multiple sensor information results in lower uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions.

Conceptualization, proposed design and theoretical investigations of an active adaptive cutting device with enhanced energy dissipation potential

Active adaptive energy dissipation systems can receive and interpret data from onboard vehicle sensors to respond to external stimuli by modifying their geometries and corresponding load bearing capacities. This is a highly promising subfield in structural crashworthiness since these devices possess multiple potential configurations, thus allowing them to achieve the preferred balance between high-capacity energy absorption and mitigation of human injury for a broad and disparate range of loading conditions. A novel cutting-based active adaptive energy absorber was conceptualized, design and theoretically assessed in this investigation with guidance from previous experimental observations and extensively validated modeling approaches. The proposed, hydraulically-driven active adaptive cutter (HAAC) was capable of transitioning between 4, 6, 8 and 12-bladed cutting deformation modes in either direction (e.g., from 6 to 12-bladed cutting or vice versa) and in a nondestructive manner. The steady-state force capacity could be varied by an average factor of 2.6, quantifying the significant range available to the HAAC regardless of extrusion material or geometry. A broad scale analytical study revealed that the proposed HAAC could eclipse the energy absorbing performance of the traditional axial crushing deformation mode for AA6061-T6 extrusions with 50.80 mm to 76.20 mm diameters and 1.59 mm to 3.18 mm wall thicknesses, with an average enhancement of 73 % calculated for the 12-bladed cutting mode. The newly proposed active adaptive capabilities allow the system to carefully balance energy absorption requirements and mitigation of occupant/pedestrian injury in a manner which the current state-of-the-art cannot achieve.

Novel Decision-Making Strategy for Connected and Autonomous Vehicles in Highway On-Ramp Merging

High-speed highway on-ramp merging is a significant challenge toward realizing fully automated driving ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">level 4</i> ). Connected Autonomous Vehicles ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAVs</i> ), that combine communication and autonomous driving technologies, may improve greatly the safety performances when performing highway on-ramp merging. However, even with the emergence of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAVs</i> , some keys constraints should be considered to achieve a safe on-ramp merging. First, human-driven vehicles will still be present on the road, and it may take decades before all the commercialized vehicles will be fully autonomous and connected. Also, onboard vehicle sensors may provide inaccurate or incomplete data due to sensors limitations and blind spots, especially in such critical situations. To resolve these issues, the present work introduces a novel solution that uses an off-board Road-Side Unit ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RSU</i> ) to realize fully automated highway on-ramp merging for connected and automated vehicles. Our proposed approach is based on an Artificial Neural Network (ANN) to predict drivers’ intentions. This prediction is used as an input state to a Deep Reinforcement Learning ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DRL</i> ) agent that outputs the longitudinal acceleration for the merging vehicle. To achieve this, we first propose a data-driven model that can predict the behavior of the human-driven vehicles in the main highway lane, with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99</i> % accuracy. We use the output of this model as input state to train a Twin Delayed Deep Deterministic Policy Gradients ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TD3</i> ) agent that learns “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safe</i> ” and “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperative</i> ” driving policy to perform highway on-ramp merging. We show that our proposed decision-making strategy improves performance compared to the solutions proposed previously.

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm.

With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles to completely cover entire unknown environments. In the previous complete coverage path planning (CCPP) research, however, autonomous vehicles need to consider mapping, obstacle avoidance, and route planning simultaneously during operating in the workspace, which results in an extremely complicated and computationally expensive navigation system. In this study, a new framework is developed in light of a hierarchical manner with the obtained environmental information and gradually solving navigation problems layer by layer, consisting of environmental mapping, path generation, CCPP, and dynamic obstacle avoidance. The first layer based on satellite images utilizes a deep learning method to generate the CCPP trajectory through the position of the autonomous vehicle. In the second layer, an obstacle fusion paradigm in the map is developed based on the unmanned aerial vehicle (UAV) onboard sensors. A nature-inspired algorithm is adopted for obstacle avoidance and CCPP re-joint. Equipped with the onboard LIDAR equipment, autonomous vehicles, in the third layer, dynamically avoid moving obstacles. Simulated experiments validate the effectiveness and robustness of the proposed framework.

H-V2X Mode 4 Adaptive Semipersistent Scheduling Control for Cooperative Internet of Vehicles

Due to the rapidly growing number of autonomous vehicles, establishing an intelligent transportation system (ITS) supported by the Internet-of-Vehicles (IoV) technology is important. The need for IoV technology is emphasized even more because recent accidents relating to autonomous vehicles show that there are limitations in the reliability of autonomous vehicles only using vehicle onboard sensors. In addition, a highly reliable support system for many vehicular applications is in strong demand, which is why there is a need for advanced vehicle-to-everything (V2X) technology. The two major types of V2X technology are dedicated short-range communication (DSRC) and cellular-V2X (C-V2X). Many vehicles are already equipped with DSRC; however, the use of C-V2X is highly supported by many mobile communication companies and the 5GAA, which is why C-V2X is expected to be very widely used. In this article, a hybrid V2X (H-V2X) scheme is proposed, which adaptively controls C-V2X to satisfy the key-performance indicator (KPI) requirements as much as possible, and uses DSRC transmission when the C-V2X required performance cannot be reliably supported. The performance analysis graphs show that the gain in performance of the proposed H-V2X scheme can provide an improved performance compared to other V2X schemes, which makes it more suitable to support reliable autonomous vehicles.

Interactive Behavior Prediction for Heterogeneous Traffic Participants in the Urban Road: A Graph-Neural-Network-Based Multitask Learning Framework

Effectively predicting interactive behaviors of traffic participants in the urban road is the key to successful decision-making and motion planning of intelligent vehicles. In this article, based on the data collected from vehicle on-board sensors, a graph-neural-network-based multitask learning framework (GNN-MTLF) is proposed to accurately predict trajectories of traffic participants with interactive behaviors. The interactive behavior considered in this research includes interactive events and trajectories that are modeled as spatial-temporal graphs using the GNN. Under the GNN-MTLF, the prediction process contains two main parts: recognition of interactive events and prediction of interactive trajectories. An integrated loss function is designed for multitask learning with the purpose of prediction and recognition. The proposed framework is verified using naturalistic driving data in the urban road. Experimental results show a superior performance of the GNN-MTLF compared to baseline methods and the potential for improving the road mobility.

Delay Aware Accident Detection and Response System Using IoT

Vehicle to vehicle communication and vehicle to roadside sensor communication are introduced with the help of hybrid ITS safety architecture.It require major investments for installation, purchase and maintenance for implementation. It represents a costbeneficial solution and can leverage the deployment of the system as a whole for roadside wireless sensor and networking technology . This paper proposepostaccident inquiry and accident precaution. A framework and convention engineering with a completely disseminated idea for secure capacity of sensor information and proficient is incorporated. This is the blend of information stockpiling and committed side of the road units as an incorporated organization component for correspondence for arrangement. At last, it depicts the product stages for vehicle on-board units and sensor hubs. model execution and exploratory proving ground including equipment.

Efficient Marginalized Particle Smoother for Indoor CSS–TOF Localization with Non-Gaussian Errors

The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ranging measurements and position estimations, Monte Carlo Bayesian smoothers are promising as involving the past, present, and future observations. However, the main problems are how to derive trackable smoothing recursions and to avoid the degeneracy of particle-based smoothed distributions. To incorporate the backward smoothing density propagation with the forward probability recursion efficiently, we propose a lightweight Marginalized Particle Smoother (MPS) for nonlinear and non-Gaussian errors mitigation. The performance of the position prediction, filtering, and smoothing are investigated in real-world experiments carried out with vehicle on-board sensors. Results demonstrate the proposed smoother enables a great tool by reducing temporal and spatial errors of mobile trajectories, with the cost of a few sequence delay and a small number of particles. Therefore, MPS outperforms the filtering and smoothing methods under weak assumptions, low computation, and memory requirements. In the view that the sampled trajectories stay numerically stable, the MPS form is validated to be applicable for time-series position tracking.

MATHEMATICAL MODEL OF ANTILOCK BRAKING SYSTEM WITH INCLUSION OF THE QUATERNION

The article discusses a description of rotation kinematics in representation of the quaternions and processing of vehicle onboard sensors signals having used Kalman Filter in relation to the task of mathematical modeling of antilock braking system of ground wheeled vehicle.

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach.

Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic®. The use of both Trucksim® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

Development and verification of cooperative adaptive cruise control via LTE‐V

In this study, the authors present a testbed platform for realising cooperative adaptive cruise control enabled by LTE-V (LTE-vehicle). The platform is developed on a platoon of vehicles, each of which is equipped with a suite of on-board sensing and computing devices for environment perception, as well as an LTE-V transceiver for high-performance vehicle-to-vehicle (V2V) communication. The hardware architecture and software architecture, especially the perception and control methods, of the platform are described. Field experiments in different road conditions are conducted to verify the feasibility of the authors’ platform. The results also show the potential of V2V communications via LTE-V in terms of improving the sensing capability of individual vehicle's on-board sensors.

Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales

Accurate and reliable vehicle velocity estimation is greatly motivated by the increasing demands of high-precision motion control for autonomous vehicles and the decreasing cost of the required multi-axis IMU sensors. A practical estimation method for the longitudinal and lateral velocities of electric vehicles is proposed. Two reliable driving empirical judgements about the velocities are extracted from the signals of the ordinary onboard vehicle sensors, which correct the integral errors of the corresponding kinematic equations on a long timescale. Meanwhile, the additive biases of the measured accelerations are estimated recursively by comparing the integral of the measured accelerations with the difference of the estimated velocities between the adjacent strong empirical correction instants, which further compensates the kinematic integral error on short timescale. The algorithm is verified by both the CarSim-Simulink co-simulation and the controller-in-the-loop test under the CarMaker-RoadBox environment. The results show that the velocities can be accurately and reliably estimated under a wide range of driving conditions without prior knowledge of the tire-model and other unavailable signals or frequently changeable model parameters. The relative estimation error of the longitudinal velocity and the absolute estimation error of the lateral velocity are kept within 2% and 0.5 km/h, respectively.