Path tracking is one of the most important aspects of autonomous vehicles. The current research focuses on designing path-tracking controllers taking into account the stability of the yaw and the nonholonomic constraints of the vehicle. In most cases, the lateral controller design relies on identifying a path reference point, the one with the shortest distance to the vehicle giving the current state of the vehicle. That restricts the controller’s ability to handle sudden changes of the trajectory heading angle. The present article proposes a new approach that imitates human behavior while driving. It is based on a discrete prediction model that anticipates the future states of the vehicle, allowing the use of the control algorithm in future predicted states augmented with the current controller output. The performance of the proposed approach is verified through several simulations on V-REP simulator with different types of maneuvers (double lane change, hook road, S road, and curved road) and a wide range of velocities. Predictive Stanley controller was used compared to the original Stanley controller. The obtained results of the proposed control approach show the advantage and the performance of the technique in terms of minimizing the lateral error and ensuring yaw stability by an average of 53% and 22%, respectively.

This article proposes a bending angle controller for soft pneumatic actuators, which could be implemented in soft robotic rehabilitation gloves to assist patients with hand impairment, such as stroke survivors. A data-driven model is used to estimate the angle as pneumatic pressure is applied to the actuator. Furthermore, a finite element model was used to manually optimize the dimensions of the actuator. An embedded flex sensor, which together with a custom testing rig, was used to gather input data for the data-driven model. This rig contains a pneumatic pressure control circuit as well as a camera for image acquisition. Collected data were fed into a linear regression model to predict the data-driven model. Experiments were carried out to validate model’s accuracy as well as modified proportional–integral–derivative controller angle controller performance. The latter controller is designed to mitigate the non-linear response of solenoid valves at different pressures of the actuator. The data-driven model along with the used controller allows more accurate estimation and quicker response.

The development of serial or parallel manipulator robots is constantly increasing due to the need for faster productivity and higher accuracy. Therefore, researchers have turned to combining both mechanisms, sharing the advantage from serial to parallel or vice versa. This paper proposes a new configuration design for a serial-parallel hybrid manipulator (SPHM) using the industrial robotic KUKA Kr6 R900 and 3-DOF parallel spherical mechanism. The Kr6 R900 has six degrees of freedom (6-DOF) divided into three joints for translation (x, y, z) and another three joints for orientation (A, B, C) of the end-effector and the 3-DOF parallel spherical mechanism with three paired links. On the contrary, each limb of the parallel spherical mechanism consists of revolute–revolute–spherical joints (3-RRS). This mechanism allows translation movement along the Z-axis and orientation movements about the X- and Y- axes. The new hybrid will enrich the serial manipulator in movement flexibility and expand the workspace for serial and parallel manipulator robots. In addition, a complete conceptual design is presented in detail for the new robot configuration with a schematic and experimental setup. Then, a comprehensive mathematical model was derived and solved. The forward, inverse kinematics, and workspace analyses were derived using the graphical solution. Additionally, the new hybrid manipulator was tested for path planning. Moreover, an experimental setup was prepared to test the selected path. Finally, the new robot configuration can enlarge the workspace of both manipulators and the selected path matched to the experimental test.

The importance of vehicle security has increased in recent years in the automotive field, drawing the attention of both the industry and academia. This is due to the rise in cybersecurity threats caused by (1) the increase in vehicle connectivity schemes, such as the Internet of Things, vehicle-to-x communication, and over-the-air updates, and (2) the increased impact of such threats because of the added functionalities that are controlled by vehicle software. These causes and threats are further amplified in autonomous vehicles, which are generally equipped with more electronic control units (ECUs) that are connected through controller area networks (CANs). Due to the holistic nature of CANs, attacks on the networks can affect the functionality of all vehicle ECUs and the whole system. This can lead to a breach of privacy, denial of services, alteration of vehicle performance, and exposure to safety threats. Although cryptographic encryption and authentication algorithms and intrusion detection systems (IDS) are currently being used to detect and prevent CAN bus attacks, they have certain limitations. Therefore, this study proposed a mitigation scheme that can detect and prevent such attacks at the ECU level, which could address the limitations of existing algorithms. This study proposed the usage of a secure boot scheme to detect and prevent the execution of malicious codes, as the presence of one or more ECUs with a malicious code is the root cause of most CAN bus attacks. Secure boot schemes apply cryptographic data integrity algorithms to ensure that only authentic and untampered software can run on the vehicle’s ECUs. The selection of an appropriate cryptographic algorithm is important because it affects the secure boot schemes’ security level and performance. Therefore, this study also tested and compared the performance of the proposed secure boot scheme with five different data security algorithms implemented using the hardware security module (HSM) of the TC399 32-bit AURIX™ TriCore™ microcontroller through an electric autonomous vehicle’s control unit. The tests showed that the two most favorable schemes with the selected hardware are the secure boot scheme with the cipher-based message authentication code (CMAC), because it possesses the highest performance with an execution rate of 26.07 (ms/MB), and the secure boot scheme with the elliptic curve digital signature algorithm (ECDSA), because it provides a higher security level with an acceptable compromise in speed. This study also introduced and tested a novel variation of the ECDSA algorithm based on the CMAC algorithm, which was found to have a 19% performance gain over the standard ECDSA-based secure boot scheme.

The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

Driver monitoring systems are becoming an essential part of Advanced Driver Assistance Systems (ADAS) safety features in modern vehicles. The U.S. National Highway Traffic Safety Administration reports that drowsy/fatigued driving results in almost 100,000 road accidents per year. Driver’s fatigue can have different causes, such as lack of sleep, long journeys, restlessness, mental pressure and alcohol consumption. Early monitoring systems relied on data from vehicle sensors, and modern systems commonly use driver’s eye tracking. Recently, there has been growing interest in utilizing machine vision and deep learning for driver monitoring. Using machine vision can create more advanced driver monitoring systems capable of detecting driver attention state as well as other features like smartphone usage while driving and seat belts. Machine vision systems usually require extensive processing power, which raises the cost of such devices. In this paper, we present a low-cost driver monitoring system using a $15 Raspberry Pi Zero 2 W board and deep learning CNN to deliver a system capable of monitoring and identifying different states of the driver like safe driving, distracted, drowsy, and smartphone usage, the system achieves an inference rate for 10 Frames Per Second (FPS) and above 90% accuracy with the testing dataset. In addition to the deep learning CNN which runs on Raspberry Pi CPU, we utilize the Raspberry Pi GPU to run a head pose estimation algorithm to boost the system’s accuracy.