The National Highway Traffic Safety Administration is also investigating the crash. Michael Butler, the driver of a Tesla Model 3 that fatally struck a woman in her Texas home, manually overrode the car's Full Self-Driving (Supervised) system by pressing the accelerator pedal to 100 percent, according to a preliminary investigation from the National Transportation Safety Board. While local authorities initially reported that the Tesla's automated driving assistance system was engaged when Butler crashed into Martha Avila's home in June, the investigation says otherwise. According to the NTSB, the acceleration disabled the FSD system and the vehicle's speed was ultimately greater than 70 mph when the crash occurred. The investigation's preliminary findings match what Tesla AI head Ashok Elluswamy shared on X in June .

That's Harder Than It Sounds Proposed regulations in China would mean the end of flush handles on car doors, with precious little time to roll out the changes. Car door handles seem innocuous. Tesla's electronic, retractable ones--since imitated by plenty of global automakers--have become a symbol of the automaker's willingness to work from design-first principles, reimagining what the car of the future might look like, electric-style. But in September, the National Highway Traffic Safety Administration launched an investigation into the Tesla 2021 Model Y's door handles. More than 140 consumers have complained to the National Highway Traffic Safety Administration (NHTSA) about the door handles, according to a Bloomberg report published last month.

In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal effects through the mediator. We study the heterogeneous treatment effect (HTE) under FD identification and introduce two debiased learners: FD-DR-Learner and FD-R-Learner. Both attain fast, quasi-oracle rates (i.e., performance comparable to an oracle that knows the nuisances) even when nuisance functions converge as slowly as n^-1/4. We provide error analyses establishing debiasedness and demonstrate robust empirical performance in synthetic studies and a real-world case study of primary seat-belt laws using Fatality Analysis Reporting System (FARS) dataset. Together, these results indicate that the proposed learners deliver reliable and sample-efficient HTE estimates in FD scenarios. The implementation is available at https://github.com/yonghanjung/FD-CATE. Keywords: Front-door adjustment; Heterogeneous treatment effects; Debiased learning; Quasi-oracle rates; Causal inference.

Despite recent advances in automated driving technology, impaired driving continues to incur a high cost to society. In this paper, we present a driving dataset designed to support the study of two common forms of driver impairment: alcohol intoxication and cognitive distraction. Our dataset spans 23.7 hours of simulated urban driving, with 52 human subjects under normal and impaired conditions, and includes both vehicle data (ground truth perception, vehicle pose, controls) and driver-facing data (gaze, audio, surveys). It supports analysis of changes in driver behavior due to alcohol intoxication (0.10\% blood alcohol content), two forms of cognitive distraction (audio n-back and sentence parsing tasks), and combinations thereof, as well as responses to a set of eight controlled road hazards, such as vehicle cut-ins. The dataset will be made available at https://toyotaresearchinstitute.github.io/IDD/.

Driverless cars are beginning to display human-like behaviors like impatience on the roads, in a sign of increased intelligence in the robotaxis. The chilling development was identified by University of San Francisco engineering Professor William Riggs, who has been studying Waymo cars since their inception. On a journey with a reporter from the San Francisco Chronicle, the pair noticed the Waymo they were traveling in crept to a rolling start at a pedestrian crossing before the person had reached the other footpath. The subtle movement was reminiscent of the way humans act behind the wheel, but a strange occurrence for the robotic Waymo, which prides itself on being safer than a driver because it errs on the side of caution and leaves no room for human error. The action of letting the foot gently off the break moments before they should to allow the car to begin creeping forward at a rolling pace displays a sense of impatience - a human reaction not previously seen in the robotic cars.

Breakthroughs, discoveries, and DIY tips sent every weekday. In 2013, Amy Cohen experienced the unthinkable for a parent. It was a mild October day in New York City and her 12-year-old son Sammy stopped by the house to grab a snack on his way from school to soccer practice. When he stepped out onto their street in Brooklyn, Sammy was struck and killed by a speeding van. "It's a horror no parent should ever experience," Cohen told Popular Science.

Driverless semitrucks raise questions about safety, reliability and the future of the trucking industry. Aurora, a leader in autonomous vehicles, has officially deployed its driverless trucks on Texas roads, marking a key milestone in the evolution of transportation. This development raises important questions about safety, reliability and the future of the trucking industry. Aurora's autonomous trucks now operate on routes between Dallas and Houston, hauling commercial loads. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up!

Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs explainable artificial intelligence (XAI) to identify key factors contributing to pedestrian fatalities across the five U.S. states with the highest crash rates (2018-2022). It compares them to the five states with the lowest fatality rates. Using data from the Fatality Analysis Reporting System (FARS), the study applies machine learning techniques-including Decision Trees, Gradient Boosting Trees, Random Forests, and XGBoost-to predict contributing factors to pedestrian fatalities. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized, while SHapley Additive Explanations (SHAP) values enhance model interpretability. The results indicate that age, alcohol and drug use, location, and environmental conditions are significant predictors of pedestrian fatalities. The XGBoost model outperformed others, achieving a balanced accuracy of 98 %, accuracy of 90 %, precision of 92 %, recall of 90 %, and an F1 score of 91 %. Findings reveal that pedestrian fatalities are more common in mid-block locations and areas with poor visibility, with older adults and substance-impaired individuals at higher risk. These insights can inform policymakers and urban planners in implementing targeted safety measures, such as improved lighting, enhanced pedestrian infrastructure, and stricter traffic law enforcement, to reduce fatalities and improve public safety.

To develop rigorous knowledge about ML models -- and the systems in which they are embedded -- we need reliable measurements. But reliable measurement is fundamentally challenging, and touches on issues of reproducibility, scalability, uncertainty quantification, epistemology, and more. This dissertation addresses criteria needed to take reliability seriously: both criteria for designing meaningful metrics, and for methodologies that ensure that we can dependably and efficiently measure these metrics at scale and in practice. In doing so, this dissertation articulates a research vision for a new field of scholarship at the intersection of machine learning, law, and policy. Within this frame, we cover topics that fit under three different themes: (1) quantifying and mitigating sources of arbitrariness in ML, (2) taming randomness in uncertainty estimation and optimization algorithms, in order to achieve scalability without sacrificing reliability, and (3) providing methods for evaluating generative-AI systems, with specific focuses on quantifying memorization in language models and training latent diffusion models on open-licensed data. By making contributions in these three themes, this dissertation serves as an empirical proof by example that research on reliable measurement for machine learning is intimately and inescapably bound up with research in law and policy. These different disciplines pose similar research questions about reliable measurement in machine learning. They are, in fact, two complementary sides of the same research vision, which, broadly construed, aims to construct machine-learning systems that cohere with broader societal values.