Showing 4 results for Autonomous Vehicles
Mr. Hamid Rahmanei, Dr. Abbas Aliabadi, Prof. Ali Ghaffari, Prof. Shahram Azadi,
Volume 13, Issue 2 (6-2023)
Abstract
The coordinated control of autonomous electric vehicles with in-wheel motors is classified as over-actuated control problems requiring a precise control allocation strategy. This paper addresses the trajectory tracking problem of autonomous electric vehicles equipped with four independent in-wheel motors and active front steering. Unlike other available methods presenting optimization formulation to handle the redundancy, in this paper, the constraints have been applied directly using the kinematic relations of each wheel. Four separate sliding mode controllers are designed in such a way that they ensure the convergence of tracking errors, in addition to incorporating the parametric and modeling uncertainties. The lateral controller is also designed to determine the front steering angles to eliminate lateral tracking errors. To appraise the performance of the proposed control strategy, a co-simulation is carried out in MATLAB/Simulink and Carsim software. The results show that the proposed control strategy has enabled the vehicle to follow the reference path and has converged the errors of longitudinal and lateral positions, velocity, heading angle, and yaw rate. Furthermore, the proposed control system shows promising results in the presence of uncertainties including the mass and moment of inertia, friction coefficient, and the wind disturbances.
Mr Seyed Amir Mohammad Managheb, Mr Hamid Rahmanei, Dr Ali Ghaffari,
Volume 14, Issue 1 (3-2024)
Abstract
The turn-around task is one of the challenging maneuvers in automated driving which requires intricate decision making, planning and control, concomitantly. During automatic turn-around maneuver, the path curvature is too large which makes the constraints of the system severely restrain the path tracking performance. This paper highlights the path planning and control design for single and multi-point turn of autonomous vehicles. The preliminaries of the turn-around task including environment, vehicle modeling, and equipment are described. Then, a predictive approach is proposed for planning and control of the vehicle. In this approach, by taking the observation of the road and vehicle conditions into account and considering the actuator constraints in cost function, a decision is made regarding the minimum number of steering to execute turn-around. The constraints are imposed on the speed, steering angle, and their rates. Moreover, the collision avoidance with road boundaries is developed based on the GJK algorithm. According to the simulation results, the proposed system adopts the minimum number of appropriate steering commands while incorporating the constraints of the actuators and avoiding collisions. The findings demonstrate the good performance of the proposed approach in both path design and tracking for single- and multi-point turns.
Ehsan Vakili, Behrooz Mashadi, Abdollah Amirkhani,
Volume 15, Issue 1 (3-2025)
Abstract
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.
Mohammad Dehghan Manshadi, Behrooz Mashadi,
Volume 16, Issue 2 (6-2026)
Abstract
Path-tracking for autonomous vehicles at physical handling limits is severely challenged by nonlinear tire saturation, which degrades conventional Active Front Steering (AFS) systems. This study proposes a hierarchical control architecture coordinating AFS, Torque Vectoring Control (TVC), and Active Rear Camber (ARC) to enhance path-tracking accuracy under limit driving conditions. An upper-level Linear Model Predictive Control (LMPC) algorithm is designed to calculate the virtual corrective yaw moment and the optimal rear camber angle. Simultaneously, a lower-level three-mode Quadratic Programming (QP) framework dynamically allocates torques based on instantaneous tire friction capacities. MATLAB/CarSim co-simulations of severe Double Lane Change (DLC) maneuvers validate the system's efficacy. Quantitatively, during a 140 km/h maneuver on dry asphalt, the proposed fully integrated system expands the maximum achievable lateral acceleration to 0.8g. Compared to the baseline AFS configuration, it significantly reduces the root-mean-square (RMS) and peak lateral tracking errors by 32% (to 0.239 m) and 27% (to 0.687 m), respectively, while concurrently decreasing the peak steering demand by 27%. Furthermore, under low-friction critical conditions (60 km/h, μ=0.5), the controller effectively limits sideslip oscillations and prevents vehicle spin-out. Ultimately, the formulated hierarchical framework manages the over-actuation dynamically, yielding a peak execution time that consumes only 74% of the real-time step limit, providing a highly viable and computationally efficient strategy for automotive Electronic Control Unit (ECU) implementation.
