Realistic modelling of pedestrians in Autonomous Vehicles (AV)s and AV testing is crucial to avoid lethal collisions in deployment. The majority of AV trajectory forecasting literature do not utilize the motion cues present in 3D human pose because it is hard to gather large datasets of articulated 3D pedestrian motion. In this thesis we discuss the difficulties in data gathering and propose a pedestrian model that overcomes the issues by utilizing various datasets and learning paradigms to learn articulated semantically reasoning pedestrian motion. We show that such learnt pedestrian models can and should be utilized in AV testing, instead of heuristics as in previous work, to test AVs on realistic and hard scenarios. We propose a framework for generating varied AV test scenarios by posing AV test case generation as a visual problem. Finally we provide a method to improve existing articulated human pose forecasting by utilizing individual specific motion cues on the fly. This thesis discusses the difficulties in articulated pedestrian sensing, proposes a pedestrian model to overcome these difficulties showing a direct use of the pedestrian model in AV testing, and shows the possible further improvements to articulated pedestrian motion forecasting should articulated models be utilized in AV trajectory planning. We hope that this work aids in the further development of articulated and semantically reasoning pedestrian models in AV testing and trajectory planning.