While advances in mobility technology including autonomous vehicles and multi-modal navigation systems can improve mobility equity for people with disabilities, these technologies depend crucially on accurate, standardized, and complete pedestrian path networks. Ad hoc collection efforts lead to a data record that is sparse, unreliable, and non-interoperable. This paper presents a sociotechnical methodology to collect, manage, serve, and maintain pedestrian path data at a statewide scale. Combining the automation afforded by computer-vision approaches applied to aerial imagery and existing road network data with the quality control afforded by interactive tools, we aim to produce routable pedestrian pathways for the entire State of Washington within approximately two years. We extract paths, crossings, and curb ramps at scale from aerial imagery, integrating multi-input segmentation methods with road topology data to ensure connected, routable networks. We then organize the predictions into project regions selected for their value to the public interest, where each project region is divided into intersection-scale tasks. These tasks are assigned and tracked through an interactive tool that manages concurrency, progress, feedback, and data management. We demonstrate that our automated systems outperform state-of-the-art methods in producing routable pathway networks, which then significantly reduces the time required for human vetting. Our results demonstrate the feasibility of yielding accurate, robust pedestrian pathway networks at the scale of an entire state. This paper intends to inform procedures for national-scale ADA compliance by providing pedestrian equity, safety, and accessibility, and improving urban environments for all users.

Understanding urban mobility patterns and analyzing how people move around cities helps improve the overall quality of life and supports the development of more livable, efficient, and sustainable urban areas. A challenging aspect of this work is the collection of mobility data by means of user tracking or travel surveys, given the associated privacy concerns, noncompliance, and high cost. This work proposes an innovative AI-based approach for synthesizing travel surveys by prompting large language models (LLMs), aiming to leverage their vast amount of relevant background knowledge and text generation capabilities. Our study evaluates the effectiveness of this approach across various U.S. metropolitan areas by comparing the results against existing survey data at different granularity levels. These levels include (i) pattern level, which compares aggregated metrics like the average number of locations traveled and travel time, (ii) trip level, which focuses on comparing trips as whole units using transition probabilities, and (iii) activity chain level, which examines the sequence of locations visited by individuals. Our work covers several proprietary and open-source LLMs, revealing that open-source base models like Llama-2, when fine-tuned on even a limited amount of actual data, can generate synthetic data that closely mimics the actual travel survey data, and as such provides an argument for using such data in mobility studies.

Authorities have confirmed that a Tesla on autopilot was partly responsible for a crash in Washington that killed a motorcyclist . Jeffrey Nissen, 28, was traveling about 15 miles northeast of Seattle when a Model S came from behind and rammed him off his bike before running him over. Investigators from the Washington State Patrol found the Tesla driver was operating on the company's'Full Self Driving' (FSD) and had looked at his cell phone while the vehicle was moving. Nissen was found under the car and pronounced dead at the scene, authorities reported. The 56-year-old driver was arrested for investigation of vehicular homicide.

Hand-intensive manufacturing processes, such as composite layup and textile draping, require significant human dexterity to accommodate task complexity. These strenuous hand motions often lead to musculoskeletal disorders and rehabilitation surgeries. We develop a data-driven ergonomic risk assessment system with a special focus on hand and finger activity to better identify and address ergonomic issues related to hand-intensive manufacturing processes. The system comprises a multi-modal sensor testbed to collect and synchronize operator upper body pose, hand pose and applied forces; a Biometric Assessment of Complete Hand (BACH) formulation to measure high-fidelity hand and finger risks; and industry-standard risk scores associated with upper body posture, RULA, and hand activity, HAL. Our findings demonstrate that BACH captures injurious activity with a higher granularity in comparison to the existing metrics. Machine learning models are also used to automate RULA and HAL scoring, and generalize well to unseen participants. Our assessment system, therefore, provides ergonomic interpretability of the manufacturing processes studied, and could be used to mitigate risks through minor workplace optimization and posture corrections.

Navy SEAL Jake Zweig responds to the intense search for the missing Titanic submarine on'Fox & Friends.' Efforts to recover the remains of the Titan submersible that suffered a catastrophic implosion near the Titanic wreckage are currently underway, and as of Sunday, had descended to the seafloor for a fourth dive. Last Thursday, the U.S. Coast Guard confirmed that a debris field located about 1,600 feet from the wreckage of the Titanic was in fact that of the missing Titan submersible. The underwater vessel was carrying five men on board when it lost contact with its surface ship about an hour and 45 minutes after descending to the Titanic. South Wellfleet, Massachusetts-based Pelagic Research Services (PRS) was contacted by OceanGate, the company behind Titan, for use of its remotely operated vehicles, or "ROVs," to assist with the search. Pelagic Research Services continues to assist the Transportation Safety Board of Canada, U.S. Coast Guard, and U.S. National Transportation Safety Board with Titan recovery efforts near the Titanic wreckage.

Altman does not appear on the Forbes billionaires list, but that doesn't mean he isn't extremely wealthy. His wide-ranging investments have included early stakes in companies like Stripe and Airbnb. "I have been an early-stage tech investor in the greatest bull market in history," he says. Now, he is putting his capital to work at a level he calls an "order of magnitude" greater than he could during his Y Combinator days. And he has been concentrating those bets into a few areas of technology he thinks will have the biggest positive impact on human affairs: AI, energy, and anti-aging biotech.

As if Elon Musk did not have enough on his plate with Twitter, Tesla is now under fire in a full-page advertisement in the New York Times that warns its'Full Self-Driving presents a life-threatening danger to child pedestrians.' The ad, which cost about $150,000, is from software maker The Dawn Project and claims to highlight safety testing conducted by the firm in October. A video of the experiment suggests the system does not register or stop for small mannequins crossing a road, according to the group. The testing involved a man driving in a Tesla on a back road and running over child-size mannequins in his path. Using the Tesla Full Self-Driving Beta 10.69.2.2, which is the latest version of the system, the vehicle collided with a 29-inch mannequin at speeds as low as 15 miles per hour and it ran over a four-foot-tall one at 20 miles per hour.

'Scout', a six-legged autonomous home-delivery robot by Amazon, started delivering packages in Snohomish County, Washington, for the first time in January 2019. Scout, like a good samaritan, used the sidewalks to travel. Upon reaching its destination, Scout would stop at the front door of its customer and open the lid so that the customer could collect their parcel. However, three years later, before Scout could fulfil its potential to be fully autonomous, Amazon just scrapped the whole project. Amazon spokesperson Alisa Carroll said there were aspects of the programme that weren't meeting customers' needs.

The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.