Mohammad Javed Ali

Ivo Batkovic, Mario Zanon, Mohammad Ali et al.

For motion planning and control of autonomous vehicles to be proactive and safe, pedestrians' and other road users' motions must be considered. In this paper, we present a vehicle motion planning and control framework, based on Model Predictive Control, accounting for moving obstacles. Measured pedestrian states are fed into a prediction layer which translates each pedestrians' predicted motion into constraints for the MPC problem. Simulations and experimental validation were performed with simulated crossing pedestrians to show the performance of the framework. Experimental results show that the controller is stable even under significant input delays, while still maintaining very low computational times. In addition, real pedestrian data was used to further validate the developed framework in simulations.

Mohammad Ali

Education sector is adopting new technologies for both teaching and learning pedagogy. Augmented Reality (AR) is a new technology that can be used in the educational pedagogy to enhance the engagement with students. Students interact with AR-based educational material for more visualization and explanation. Therefore, the use of AR in education is becoming more popular. However, most researches narrate the use of AR technologies in the field of English, Maths, Science, Culture, Arts, and History education but the absence of ethical education is visible. In our paper, we design the system and develop an AR-based mobile game model in the field of Ethical education for pre-primary students. Students from pre-primary require more interactive lessons than theoretical concepts. So, we use AR technology to develop a game which offers interactive procedures where students can learn with fun and engage with the context. Finally, we develop a prototype that works with our research objective. We conclude our paper with future works.

Tommy Tram, Ivo Batkovic, Mohammad Ali et al.

In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on model predictive control. Traffic is simulated with numerous predefined driver behaviors and intentions, and the performance of the proposed decision algorithm was evaluated against another controller. The results show that the proposed decision algorithm yields shorter training episodes and an increased performance in success rate compared to the other controller.