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Robust object detection is critical for autonomous driving and mobile robotics, where accurate detection of vehicles, pedestrians, and obstacles is essential for ensuring safety. Despite the advancements in object detection transformers (DETRs), their robustness against adversarial attacks remains underexplored. This paper presents a comprehensive evaluation of DETR model and its variants under both white-box and black-box adversarial attacks, using the MS-COCO and KITTI datasets to cover general and autonomous driving scenarios. We extend prominent white-box attack methods (FGSM, PGD, and CW) to assess DETR vulnerability, demonstrating that DETR models are significantly susceptible to adversarial attacks, similar to traditional CNN-based detectors. Our extensive transferability analysis reveals high intra-network transferability among DETR variants, but limited cross-network transferability to CNN-based models. Additionally, we propose a novel untargeted attack designed specifically for DETR, exploiting its intermediate loss functions to induce misclassification with minimal perturbations. Visualizations of self-attention feature maps provide insights into how adversarial attacks affect the internal representations of DETR models. These findings reveal critical vulnerabilities in detection transformers under standard adversarial attacks, emphasizing the need for future research to enhance the robustness of transformer-based object detectors in safety-critical applications.

In this paper, we investigate the robustness of an LSTM neural network against noise injection attacks for electric load forecasting in an ideal microgrid. The performance of the LSTM model is investigated under a black-box Gaussian noise attack with different SNRs. It is assumed that attackers have just access to the input data of the LSTM model. The results show that the noise attack affects the performance of the LSTM model. The load prediction means absolute error (MAE) is 0.047 MW for a healthy prediction, while this value increases up to 0.097 MW for a Gaussian noise insertion with SNR= 6 dB. To robustify the LSTM model against noise attack, a low-pass filter with optimal cut-off frequency is applied at the model's input to remove the noise attack. The filter performs better in case of noise with lower SNR and is less promising for small noises., Comment: 6 pages, 9 figures

This letter deals with the problem of quantifying resiliency of Networked Control Systems (NCSs) to Denial-of-Service (DoS) attacks and variable network delays. Internal exponential stability and $\mathcal{L}_2$ external stability are studied. The closed-loop system is augmented with an auxiliary timer variable and analyzed in a hybrid system framework. Lyapunov-like conditions are given to ensure $0$-input global exponential stability and $\mathcal{L}_2$ external stability. A computationally affordable algorithm based on linear matrix inequalities is devised to provide trade-off curves between maximum length of DoS attacks and largest network delays. Finally, the effectiveness of the proposed approach is shown in a numerical example., Comment: Matches the published version

Output feedback control design for linear time-invariant systems in the presence of sporadic measurements and exogenous perturbations is addressed. To cope with the sporadic availability of measurements of the output, a hybrid dynamic output feedback controller equipped with a holding device whose state is reset when a new measurement is available is designed. The closed-loop system, resulting from the interconnection of the controller and the plant, is augmented with a timer variable triggering the arrival of new measurements and its properties are analyzed using hybrid system tools. Building upon Lyapunov theory for hybrid systems, sufficient conditions for internal and L2 input-to-output stability are proposed. An LMI-based design methodology for the co-design of the gains of the controller and the parameters of the holding device is presented. The effectiveness of the proposed LMI-based design approach is showcased in a numerical example., Comment: Extended version of the paper accepted for publication in the IEEE Transactions on Automatic Control

Recent research focused on improving the vehicle-to-vehicle communication (V2V) based on the 5G technology. The V2V application is important because it will reduce the risk of accidents up to 70%-80%, improve traffic management, reduce congestion, and improve fuel consumption. Autonomous vehicles applications require a high bandwidth transmission channel where the 5G communication channel would be a reliable solution to support this disruptive technology. The dedicated short-range communications (DSRC), which is characterized with a frequency bandwidth of 5.9 gigahertz (GHz) (4G spectrum), was used as vehicular connectivity with a bandwidth of up to 200 megabytes per second (mb/s) and limited capacity. The 5G band can support connected multiple autonomous vehicles with high data rates and large bandwidth. In this study, the 5G communication channel is considered as vehicular connectivity with high bandwidth in the millimeter waves spectrum range. The quality of 5G wireless communication channels between connected vehicles possibly be affected by weather conditions such as rain, snow, fog, dust, and sand. In this paper, we estimate the effect of dust and sand on the propagation of millimeter waves. The Mie model is used to investigate the effect of dust and sand storms on the propagating mm-waves. The effect of dust and sand on the communication path loss of DSRC and 5G frequency band is investigated in the case of urban freeway and rural highway settings. Results show that the attenuation of dust and sand is changed when the particle size of sand, frequency of propagating wave, and concentration of dust are changed. Finally, the new model of link margin is created to estimate the effect of dust and sand on DSCR (5.9 GHz) and 5G (28 GHz) communication path loss., Comment: 6 pages, 4 figures

Wireless connections are a communication channel used to support different applications in our life such as microwave connections, mobile cellular networks, and intelligent transportation systems. The wireless communication channels are affected by different weather factors such as rain, snow, fog, dust, and sand. This effect is more evident in the high frequencies of the millimeter-wave (mm-wave) band. Recently, the 5G opened the door to support different applications with high speed and good quality. A recent study investigates the effect of rain and snow on the 5G communication channel to reduce the challenge of using high millimeter-wave frequencies. This research investigates the impact of dust and sand on the communication channel of 5G mini links using Mie scattering model to estimate the propagating wave's attenuation by computing the free space loss of a dusty region. Also, the cross-polarization of the propagating wave with dust and sand is taken into account at different distances of the propagating length. Two kinds of mini links, ML-6363, and ML-6352, are considered to demonstrate the effect of dust and sand in these specific operating frequency bands. The 73.5 GHz (V-band) and (21.5GHz (K-band) are the ML-6352 and ML-6363 radio frequency, respectively. Also, signal depolarization is another important radio frequency transmission parameter that is considered heroin. The numerical and simulation results show that the 5G ML-6352 is more effect by dust and sand than ML6363. The 5G toolbox is used to build the communication system and simulate the effect of the dust and sand on the different frequency bands., Comment: 6 pages, 10 figures, IEEE WiSEE 2021

This paper deals with the design of resilient Cooperative Adaptive Cruise Control (CACC) for homogeneous vehicle platoons in which communication is vulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as consecutive packet dropouts. We present a controller tuning procedure based on linear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks, while guaranteeing performance and string stability. The design procedure returns controller gains and gives a lower bound on the maximum allowable number of successive packet dropouts. A numerical example is employed to illustrate the effectiveness of the proposed approach., Comment: Published version

Operation in a real world traffic requires autonomous vehicles to be able to plan their motion in complex environments (multiple moving participants). Planning through such environment requires the right search space to be provided for the trajectory or maneuver planners so that the safest motion for the ego vehicle can be identified. Given the current states of the environment and its participants, analyzing the risks based on the predicted trajectories of all the traffic participants provides the necessary search space for the planning of motion. This paper provides a fresh taxonomy of safety / risks that an autonomous vehicle should be able to handle while navigating through traffic. It provides a reference system architecture that needs to be implemented as well as describes a novel way of identifying and predicting the behaviors of the traffic participants using classic Multi Model Adaptive Estimation (MMAE). Preliminary simulation results of the implemented model are included.

In this paper, we focus on developing driver-in-the loop fuel economic control strategy for multiple connected vehicles. The control strategy is considered to work in a driver assistance framework where the controller gives command to a driver to follow while considering the ability of the driver in following control commands. Our proposed method uses vehicle-to-vehicle (V2V) communication, exploits traffic lights' Signal Phase and Timing (SPAT) information, models driver error injection with Markov chain, and employs scenario tree based stochastic model predictive control to improve vehicle fuel economy and traffic mobility. The proposed strategy is decentralized in nature as every vehicle evaluates its own strategy using only local information. Simulation results show the effect of consideration of driver error injection when synthesizing fuel economic controllers in a driver assistance fashion.