Human trust in autonomous vehicles is built upon their safe and secure operability in the most ethical, law abiding manner possible. Despite the technological advancements that autonomous vehicles are equipped with, their perplexing operation on roads often give away telltale signs of underlying vulnerabilities to threats and attack strategies which can flag their anti-autonomous traits. Anti-autonomy refers to any conduct of autonomous vehicles that goes against the principles of autonomy and subsequently resulting in their immobilized operations during unexpected roadway situations. The concept of trust is fluid, which is made complicated by anti-autonomous behavior of autonomous vehicles and affects the dimensions of intentionality, human interaction, and adoption of autonomous vehicles. Trust is impacted by intentionality, safety and risks associated with autonomous vehicles and their overall perception by human drivers, pedestrians and bicyclist sharing the roads with them. The presence of collision data involving human drivers of other cars, pedestrian, bicyclists, resulting in injuries and damages poses a significant negative impact on trust in autonomous vehicle technology. This dissertation presents and evaluates a new and innovative anti-autonomy NoTrust Artificial Neural Network model by utilizing collision data reports involving autonomous vehicles provided by California DMV from October 2014 to March 2020, which is the latest reported data. This data was augmented, labelled, classified, pre-processed, and then applied towards creation of the NoTrust ANN model using linear sequential model libraries in Keras over Tensorflow. This model was used to predict trust in autonomous vehicles. The trained model was able to achieve 100% accuracy, which was evident in the results of model compilation and training, plots of validation and training accuracies and losses. Model evaluations and predictions were used to comprehend characteristics of trust, intentionality and anti-autonomy and helped establish a relationship between them and reflected inter-dependencies among trust, intentionality, anti-autonomy, risk, and safety. Additional analyses of collision reports data was performed and the impact of several contributing factors of collisions such as vehicle driving mode, damages sustained by the vehicle, pedestrian and bicyclist involved in collisions, weather conditions, roadway surface, lighting conditions, movement of vehicle preceding collision and type of collisions was illustrated.