Intelligent Management System for Driverless Vehicles

This research addresses concerns related to driverless vehicles by proposing the development of an Intelligent Management System (IMS). Emphasised in 'The Pathway to Driverless Cars Summary report and action plan' by the UK Department of Transport, key areas for improvement lie in vehicle reliability, maintenance, and passenger safety. The study targets compliance with Society of Automotive Engineers (SAE) Level 5 automation, concentrating on fully autonomous vehicles to enhance commuter satisfaction and overall vehicle performance. Despite advancements, challenges such as on-road safety and integration persist. The research unfolds through a two-stage development process aimed at achieving an Intelligent Management System for Driverless Vehicles (IMSDV). The initial stage, described in chapter 3 involves the creation of a 'Single Seat Driverless Pod' as a test apparatus, simulating various features found in existing driverless vehicles. This includes the development of mechanical steering components and a control system incorporating electronic hardware, sensors, actuators, controllers, wireless remote access, and software. The subsequent phase, described in chapter 4 focuses on autonomous navigation using Google Maps, intelligent motion control, localisation, and tracking algorithms within the driverless pod. The latter chapters of the thesis present the investigation of possible improvements in steering system components. A novel encapsulated vehicle wheel condition monitoring system, integrating the Internet of Things (IoT), is proposed to enhance maintainability, reliability, and passenger safety for driverless vehicles. Testing and validation are conducted in two segments. The driverless pod undergoes initial testing to validate its features and generate data for further sub-system development. Separately, the IoT-based monitoring system undergoes individual testing. The final step involves integrating the IoT capabilities into the driverless pod, testing the sub-system, and capturing relevant data. The thesis outlines the research scope, emphasising significant contributions, with a particular focus on the monitoring system for steering components in driverless vehicles, employing embedded IoT technology. This augmentation, alongside other original contribution, is strategically poised to enhance the maintainability, reliability, and safety of driverless vehicles at SAE Level 5. The concluding chapter succinctly revisits these distinctive contributions and additionally provides recommendations for advancing intelligent management systems for driverless vehicles

Optimizing the Steering of Driverless Personal Mobility Pods with a Novel Differential Harris Hawks Optimization Algorithm (DHHO) and Encoder Modeling

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper has been conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. The MATLAB source code and the result files for the proposed algorithm are available at https://github.com/MohamedRedaMu/DHHO

Development of a Driverless Personal Mobility Pod

The paper describes design and development of a new `Personal Mobility Pod' using low cost systems proposed for use in urban areas. Recent studies have shown increased use of personal mobility, suggesting the scope for further research. Adding to Mobility-on-Demand and vehicle share, such mobility pods could bridge the gap in driverless vehicle research and possibly be a solution to road traffic and congestion in urban areas. The proposed platform is a combination of sensory fusion with feedback managed by a main controller. The navigation system considers offline mapping and localisation with user interface, illustrating waypoints through Google Maps. A Pure Pursuit technique is used to track the vehicle along the given path. The scooters robust, reliable, safe design allows operation in various terrains. The developed platform is moreover proposed as a suitable test platform for driverless vehicle sub-system for testing and experimentation. The reliability of the pod has been tested and validated in two stages: laboratory testing and field testing