Automotive Science and Engineering

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This paper experimentally investigates the trafficability of a small tracked vehicle on a slope. An increase in the angle of slope inclination may divert the vehicle from its path. In other words, the deviation of the vehicle is due to a sudden increase in the yaw angle. Also, the tip-over occurs at a specific slope angle. The locomotion of the small tracked vehicle on soils with different terramechanics (such as cohesion, internal friction angle, cohesive modulus, and friction modulus) is also simulated to evaluate its slope-traversing performance. Moreover, the impact of velocity and soil type on traversing a slope is measured. The proposed yaw angle control system is modeled for controlling the yaw angle of the tracked vehicle. This controller is designed through co-simulation. It keeps the tracked vehicle at zero yaw angle to achieve straight locomotion on slopes. It is compared to the PI, PID, and fuzzy controllers. The response of this controller is faster than PI and PID controllers. A Comparison between fuzzy and proposed yaw angle controller yields almost similar responses. The mechanism of the proposed yaw angle controller is also easier to understand. The precision of the controller's performance is measured by simulating over different terrains.

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As electric vehicles become more popular, we need to keep improving the lithium-ion batteries that power them. Electrochemical impedance spectroscopy (EIS) is used based on a discrete random binary sequence (DRBS) to reduce excitation time in the low-frequency region and excite the input of the battery. In this paper, voltage and current signals are processed with wavelet transform for impedance evaluation. In using wavelet transform, choosing the most optimal mother wavelet is crucial for impedance evaluation since different mother wavelets can produce different results. We aim to compare three types of continuous Morse mother wavelet, continuous Morlet, and continuous lognormal wavelet, which are among the most important mother wavelets, to determine the best method for impedance evaluation. We used the dynamic time-warping algorithm to quantify the difference between the initial values obtained from standard laboratory equipment and the impedance evaluation through three different continuous wavelets. Our proposed method (lognormal wavelet) has the lowest difference (3.4086) from the initial values compared to the Morlet (3.5504), and Morse (3.5457) methods. As a result, our simulation shows that the lognormal wavelet transform is the best method for impedance evaluation compared to Morlet and Morse wavelets.

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Ensuring that ethically sound decisions are made under complex, real-world conditions is a central challenge in deploying autonomous vehicles (AVs). This paper introduces a human-centric risk mitigation framework using Deep Q-Networks (DQNs) and a specially designed reward function to minimize the likelihood of fatal injuries, passenger harm, and vehicle damage. The approach uses a comprehensive state representation that captures the AV’s dynamics and its surroundings (including the identification of vulnerable road users), and it explicitly prioritizes human safety in the decision-making process. The proposed DQN policy is evaluated in the CARLA simulator across three ethically challenging scenarios: a malfunctioning traffic signal, a cyclist’s sudden swerve, and a child running into the street. In these scenarios, the DQN-based policy consistently minimizes severe outcomes and prioritizes the protection of vulnerable road users, outperforming a conventional collision-avoidance strategy in terms of safety. These findings demonstrate the feasibility of deep reinforcement learning for ethically aligned decision-making in AVs and point toward a pathway for developing safer and more socially responsible autonomous transportation systems.