Autonomous Vehicles (AVs) have shown indelible and revolutionary effects on accident reduction and more efficient use of travel time, with outstanding socio‐economic impact. Despite these benefits, to make AVs accepted by a wide demographic and produce them on an industrial scale with a reasonable price, there are still a number of technological and social challenges that need to be tackled. Path Tracking Controller (PTC) of AVs is one of the high potential subsystems that can be further improved in order to achieve more accurate, robust and comfortable tracking performance. This study provides a critical review and simulation study of several selected techniques used for the design of PTC of AVs. The AVs are assumed to have limited controllability due to non‐holonomic constraints, such as car‐like vehicles and differential drive mobile robots. A detailed discussion will be provided on the simulation outcomes as well as the pros and cons of each technique for the sake of implementation and improvement of state‐of‐the‐art PTC.

No abstract

No abstract

Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker's satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

No abstract

No abstract

No abstract

Nowadays, classical washout filters are extensively used in commercial motion simulators. Even though there are several advantages for classical washout filters, such as short processing time, simplicity and ease of adjustment, they have several shortcomings. The main disadvantage is the fixed scheme and parameters of the classical washout filter cause inflexibility of the structure and thus the resulting simulator fails to suit all circumstances. Moreover, it is a conservative approach and the platform cannot be fully exploited. The aim of this research is to present a fuzzy logic approach and take the human perception error into account in the classical motion cueing algorithm, in order to improve both the physical limits of restitution and realistic human sensations. The fuzzy compensator signal is applied to adjust the filtered signals on the longitudinal and rotational channels online, as well as the tilt coordination to minimize the vestibular sensation error below the human perception threshold. The results indicate that the proposed fuzzy logic controllers significantly minimize the drawbacks of having fixed parameters and conservativeness in the classical washout filter. In addition, the performance of motion cueing algorithm and human perception for most occasions is improved.