This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing the engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as the affecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the VLV. We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived at each second. Thus, three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. A performance comparison between predicted and constant ACCSSP’s indicates that the predictive model outperforms constant ACCSSP.
Audio splicing is one of the most common manipulation techniques in the audio forensic world. In this paper, the magnitudes of acoustic channel impulse response and ambient noise are considered as the environmental signature and used to authenticate the integrity of query audio and identify the spliced audio segments. The proposed scheme firstly extracts the magnitudes of channel impulse response and ambient noise by applying the spectrum classification technique to each suspected frame. Then, correlation between the magnitudes of query frame and reference frame is calculated. An optimal threshold determined according to the statistical distribution of similarities is used to identify the spliced frames. Furthermore, a refining step using the relationship between adjacent frames is adopted to reduce the false positive rate and false negative rate. Effectiveness of the proposed method is tested on two data sets consisting of speech recordings of human speakers. Performance of the proposed method is evaluated for various experimental settings. Experimental results show that the proposed method not only detects the presence of spliced frames, but also localizes the forgery segments. Comparison results with previous work illustrate the superiority of the proposed scheme.
Electric Vehicles' Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle network communication. Simplicity, robustness, and suitability for real-time systems are the salient features of CAN bus. Unfortunately, the CAN bus protocol is vulnerable to various cyberattacks due to the lack of a message authentication mechanism in the protocol itself, paving the way for attackers to penetrate the network. This paper proposes a new effective anomaly detection model based on a modified one-class support vector machine in the CAN traffic. The proposed model makes use of an improved algorithm, known as the modified bat algorithm, to find the most accurate structure in the offline training. To evaluate the effectiveness of the proposed method, CAN traffic is logged from an unmodified licensed electric vehicle in normal operation to generate a dataset for each message ID and a corresponding occurrence frequency without any attacks. In addition, to measure the performance and superiority of the proposed method compared to the other two famous CAN bus anomaly detection algorithms such as Isolation Forest and classical one-class support vector machine, we provided Receiver Operating Characteristic (ROC) for each method to quantify the correctly classified windows in the test sets containing attacks. Experimental results indicate that the proposed method achieved the highest rate of True Positive Rate (TPR) and lowest False Positive Rate (FPR) for anomaly detection compared to the other two algorithms. Moreover, in order to show that the proposed method can be applied to other datasets, we used two recent popular public datasets in the scope of CAN bus traffic anomaly detection. Benchmarking with more CAN bus traffic datasets proves the independency of the proposed method from the meaning of each message ID and data field that make the model adaptable with different CAN datasets.
<div class="section abstract"><div class="htmlview paragraph">The automotive industry widely accepted the launch of electric vehicles in the global market, resulting in the emergence of many new areas, including battery health, inverter design, and motor dynamics. Maintaining the desired thermal stress is required to achieve augmented performance along with the optimal design of these components. The HVAC system controls the coolant and refrigerant fluid pressures to maintain the temperatures of [Battery, Inverter, Motor] in a definite range. However, identifying the prominent factors affecting the thermal stress of electric vehicle components and their effect on temperature variation was not investigated in real-time. Therefore, this article defines the vector electric vehicle thermal operating point (EVTHOP) as the first step with three elements [instantaneous battery temperature, instantaneous inverter temperature, instantaneous stator temperature]. As a next step, a novel deep learning model was proposed utilizing the integrated functions of MATLAB, which predicts the vector EVTHOP mapping the elements of [Body module, Driver behavior, Environmental factors], which represent the dynamic state of the system. The trained models were developed using real-time data retrieved by driving the test vehicle 2023 Cadillac Lyriq, provided by General Motors Inc. Since the data retrieved is time-series, the trained functions were developed using the known established method NARX. The Error vector was defined by estimating the conformance of actual and predicted values. The performance of NARX was done by analyzing the Error using the known statistical techniques (RMSE, Area under the curve, Smoothness measure: RSquare). The data snippets for 100 seconds were selected randomly to validate the deep learning model, and it was observed that statistical analysis of the Error resulted [RMSE < 0.2; Area < 632, RSquare > 0.7] in all scenarios. Thus, the developed predictive model was assumed to produce satisfactory results in predicting the vector EVTHOP.</div></div>
Autonomous vehicle is becoming a complex cyber-physical system with many interfaces to the external world like Wi-Fi, Bluetooth, cellular, and vehicle to anything (v2x) networks. These interfaces open new attack surfaces that can put the onboard sensors used in autonomous driving at risk of internal and external cyber-attacks that are capable of manipulating the sensor data. Since the control algorithms that define the autonomous vehicle behavior rely on the data from these onboard sensors like LiDAR, camera and RADAR, failure to secure the sensor data could lead to erroneous decisions and may result in fatal accidents. In this paper, we propose a 3D QIM based data- hiding technique to secure the raw data from LiDAR sensor. The proposed technique detects the tampering of the LiDAR sensor data and also locates the tampered region. The evaluation of the proposed method on KITTI dataset showed that the method can successfully detect and localize insider data tampering attacks such as fake target insertion (FTI) and valid target deletion (VTD).
An audio recording is subject to a number of possible distortions and artifacts. Consider, for example, artifacts due to acoustic reverberation and background noise. The acoustic reverberation depends on the shape and the composition of the room, and it causes temporal and spectral smearing of the recorded sound. The background noise, on the other hand, depends on the secondary audio source activities present in the evidentiary recording. Extraction of acoustic cues from an audio recording is an important but challenging task. Temporal changes in the estimated reverberation and background noise can be used for dynamic acoustic environment identification (AEI), audio forensics, and ballistic settings. We describe a statistical technique based on spectral subtraction to estimate the amount of reverberation and nonlinear filtering based on particle filtering to estimate the background noise. The effectiveness of the proposed method is tested using a data set consisting of speech recordings of two human speakers (one male and one female) made in eight acoustic environments using four commercial grade microphones. Performance of the proposed method is evaluated for various experimental settings such as microphone independent, semi- and full-blind AEI, and robustness to MP3 compression. Performance of the proposed framework is also evaluated using Temporal Derivative-based Spectrum and Mel-Cepstrum (TDSM)-based features. Experimental results show that the proposed method improves AEI performance compared with the direct method (i.e., feature vector is extracted from the audio recording directly). In addition, experimental results also show that the proposed scheme is robust to MP3 compression attack.
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