Early detection of driver mental fatigue is one of the active areas of research in smart and intelligent vehicles. There are various methods, based on measuring the physiological characteristics of the driver utilising sensors and computer vision, proposed in the literature. In general, driver behaviour is unpredictable that can suddenly change the nature of driving and dynamics under mental fatigue. This results in sudden variations in driver body posture and head movement, with consequent inattentive behaviour that can end in fatal accidents and crashes. In the process of delineating the different driving patterns of driver states while active or influenced by mental fatigue, this paper contributes to advancing direct measurement approaches. In the novel approach proposed in this paper, driver mental fatigue and drowsiness are measured by monitoring driver’s head posture motions using XSENS motion capture system. The experiments were conducted on 15 healthy subjects on a MATHWORKS driver-in-loop (DIL) simulator, interfaced with Unreal Engine 4 studio. A new modified bidirectional long short-term memory deep neural network, based on a rectified linear unit layer, was designed, trained and tested on 3D time-series head angular acceleration data for sequence-to-sequence classification. The results showed that the proposed classifier outperformed state-of-art approaches and conventional machine learning tools, and successfully recognised driver’s active, fatigue and transition states, with overall training accuracy of 99.2%, sensitivity of 97.54%, precision and F1 scores of 97.38% and 97.46%, respectively. The limitations of the current work and directions for future work are also explored.

Distraction, misjudgement and driving mistakes can significantly affect a driver, resulting in an increased risk of accidents. There are diverse factors that can cause mistakes in driving such as unfamiliarity with the road, situation unawareness, fatigue, stress, and drowsiness. In emerging smart cars, sensing, actuation, advanced signal processing and machine learning are deployed to reduce the impact of driving errors by monitoring the state of the driver in real-time, detecting the mistakes, and deploying necessary actions to counteract them. Such strategies are collectively known as human-machine shared driving. Towards a better understanding of the developments taken place in this domain, as well as identifying gaps and trends in this discipline, a systematic review of the major studies and developments reported in the literature is conducted. The study is based on 155 papers of human-machine shared driving, selected through a thorough and comprehensive search of the literature. The review demonstrates that shared control approaches are mostly dependent on vehicle and environmental data obtained through various sensors. The majority of methods deploy active shared control by leveraging longitudinal and lateral dynamics. However, the precise recognition of driver’s intent and actions, accurate estimation of situation awareness, and modelling the trust between driver and automation are still major challenges preventing timely transition of control from the driver to machine or vice-versa, and resulting in fatal accidents. Major challenges in human-machine shared driving are identified and potential future directions of the work are explored.

An energy efficient heterogeneous multirotor unmanned aerial system (UAS) is presented in this paper, wherein, the aerodynamical characteristics of both helicopter and quadrotor are obtained in a single multirotor design. It features the energy efficiency and endurance of a helicopter, while keeping the mechanical simplicity, control and maneuverability of a quadrotor; employing a single large central rotor to get majority of the lift and four small arm canted rotors for control. Developing the stable and robust control strategy requires the accurate model of system. Due to the added mechanical complexities of the new design including the existence of couplings and gyroscopics, the modelling through the dynamic equations of the multirotor would not be possible in providing accurate results. Therefore, precise system modelling is required for the development of stable and robust control strategy. This paper proposes a novel system identification method with the objective to experimentally estimation of the precise dynamic model of the heterogeneous multirotor. The approach comprises of the utilization of input excitation signals, frequency sampling filter and derivation of transfer functions through complex curve fitting method. To validate the accuracy of the obtained transfer functions, the experimentally auto-tuned PID controllers are implemented over the transfer functions. Custom designed fight controller is used to experimentally implement the proposed idea. Presented results demonstrate the efficacy of the proposed approach for heterogeneous multirotor UAS.

Unlike bigger aircraft, the small fixed-wing unmanned aerial vehicles face significant stability challenges in a turbulent environment. To improve the flight performance, a fixed-wing UAV with segmented aileron control surfaces has been designed and deployed. A total of four ailerons are attached to the main wing and grouped into inner and outer aileron pairs. The controllers are automatically tuned by utilizing the frequency response data obtained via the frequency sampling filter and the relay with embedded integrator experiments. The hardware validation experiments are performed in the normal and turbulent flight environments under three configurations: inner aileron pair only, outer aileron pair only and collective actuation of all the aileron pairs. The error-threshold-based control is introduced to handle collective actuation of aileron pairs. The experiments have manifested that the collective usage of all aileron segments improves the roll attitude stability by a margin of $38.69\%$ to $43.51\%$ when compared to the independent actuation of aileron pairs in a turbulent atmosphere.

Driver mental fatigue is considered a major factor affecting driver behavior that may result in fatal accidents. Several approaches are addressed in the literature to detect fatigue behavior in a timely manner through either physiological or in-vehicle measurement methods. However, the literature lacks the implementation of hybrid approaches that combine the strength of individual approaches to develop a robust fatigue detection system. In this regard, a hybrid temporal approach is proposed in this paper to detect driver mental fatigue through the combination of driver postural configuration with vehicle longitudinal and lateral behavior on a study sample of 34 diverse participants. A novel fully adaptive symbolic aggregate approximation (faSAX) algorithm is proposed, which adaptively segments and assigns symbols to the segmented time-variant fatigue patterns according to the discrepancy in postural behavior and vehicle parameters. These multivariate symbols are then combined to prepare the bag of words (text format dataset), which is further processed to generate a semantic report of the driver’s status and vehicle situations. The report is then analyzed by a natural language processing scheme working as a sequence-to-label classifier that detects the driver’s mental state and a possible outcome of the vehicle situation. The ground truth of report formation is validated against measurements of mental fatigue through brain signals. The experimental results show that the proposed hybrid system successfully detects time-variant driver mental fatigue and drowsiness states, along with vehicle situations, with an accuracy of 99.6% compared to state-of-the-art systems. The limitations of the current work and directions for future research are also explored.