Motor vehicle crashes remain a leading cause of injury and death worldwide, necessitating data-driven approaches to understand and mitigate crash severity. This study introduces a curated dataset of more than 3 million people involved in accidents in Ohio over six years (2017-2022), aggregated to more than 2.3 million vehicle-level records for predictive analysis. The primary contribution is a transparent and reproducible methodology that combines Automated Machine Learning (AutoML) and explainable artificial intelligence (AI) to identify and interpret key risk factors associated with severe crashes. Using the JADBio AutoML platform, predictive models were constructed to distinguish between severe and non-severe crash outcomes. The models underwent rigorous feature selection across stratified training subsets, and their outputs were interpreted using SHapley Additive exPlanations (SHAP) to quantify the contribution of individual features. A final Ridge Logistic Regression model achieved an AUC-ROC of 85.6% on the training set and 84.9% on a hold-out test set, with 17 features consistently identified as the most influential predictors. Key features spanned demographic, environmental, vehicle, human, and operational categories, including location type, posted speed, minimum occupant age, and pre-crash action. Notably, certain traditionally emphasized factors, such as alcohol or drug impairment, were less influential in the final model compared to environmental and contextual variables. Emphasizing methodological rigor and interpretability over mere predictive performance, this study offers a scalable framework to support Vision Zero with aligned interventions and advanced data-informed traffic safety policy.

Articulated multi-axle vehicles are interesting from a control-theoretic perspective due to their peculiar kinematic offtracking characteristics, instability modes, and singularities. Holonomic and nonholonomic constraints affecting the kinematic behavior is investigated in order to develop control-oriented kinematic models representative of these peculiarities. Then, the structure of these constraints is exploited to develop an iterative algorithm to symbolically derive yaw-plane kinematic models of generalized $n$-trailer articulated vehicles with an arbitrary number of multi-axle vehicle units. A formal proof is provided for the maximum number of kinematic controls admissible to a large-scale generalized articulated vehicle system, which leads to a generalized Ackermann steering law for $n$-trailer systems. Moreover, kinematic data collected from a test vehicle is used to validate the kinematic models and, to understand the rearward yaw rate amplification behavior of the vehicle pulling multiple simulated trailers.

The Walmart pilot initially will use human-driven vehicles operated to simulate how a self-driving vehicle would behave, Ford said. Ford has said it expects to launch commercial production of automated vehicles by 2021. Ford and its partners are using Miami as a testing ground for automated delivery service ideas and automated vehicle technology. The new pilot project will offer customers delivery by Postmates of goods ordered at Walmart stores. Brian Wolf, an executive of Ford's autonomous vehicle unit, wrote in a blog post that the companies will work over the next "couple of months" to figure out what goods can be delivered successfully, especially perishable groceries.

Ford said it would invest $4 billion through 2023 in its newly formed autonomous vehicle unit, Ford Autonomous Vehicles, as it looks to produce self-driving cars in the next three years. The No. 2 U.S. automaker said the new unit would include self-driving systems integration, autonomous vehicle research and advanced engineering. It will go up against Google parent Alphabet's Waymo, Uber, Tesla and doxens of other into the lucrative market. Ford recently announced a collaboration with Miami-Dade County in Florida to test its self-driving vehicle business model on the streets of Miami and Miami Beach. The No. 2 U.S. automaker said the new unit would include self-driving systems integration, autonomous vehicle research and advanced engineering.