There is a rising interest in using artificial intelligence (AI)-powered safety analytics to predict accidents in the trucking industry. Companies may face the practical challenge, however, of not having enough data to develop good safety analytics models. Although pretrained models may offer a solution for such companies, existing safety research using transfer learning has mostly focused on computer vision and natural language processing, rather than accident analytics. To fill the above gap, we propose a pretrain-then-fine-tune transfer learning approach to help any company leverage other companies' data to develop AI models for a more accurate prediction of accident risk. We also develop SafeNet, a deep neural network algorithm for classification tasks suitable for accident prediction. Using the safety climate survey data from seven trucking companies with different data sizes, we show that our proposed approach results in better model performance compared to training the model from scratch using only the target company's data. We also show that for the transfer learning model to be effective, the pretrained model should be developed with larger datasets from diverse sources. The trucking industry may, thus, consider pooling safety analytics data from a wide range of companies to develop pretrained models and share them within the industry for better knowledge and resource transfer. The above contributions point to the promise of advanced safety analytics to make the industry safer and more sustainable.

The transportation industry, particularly the trucking sector, is prone to workplace accidents and fatalities. Accidents involving large trucks accounted for a considerable percentage of overall traffic fatalities. Recognizing the crucial role of safety climate in accident prevention, researchers have sought to understand its factors and measure its impact within organizations. While existing data-driven safety climate studies have made remarkable progress, clustering employees based on their safety climate perception is innovative and has not been extensively utilized in research. Identifying clusters of drivers based on their safety climate perception allows the organization to profile its workforce and devise more impactful interventions. The lack of utilizing the clustering approach could be due to difficulties interpreting or explaining the factors influencing employees' cluster membership. Moreover, existing safety-related studies did not compare multiple clustering algorithms, resulting in potential bias. To address these issues, this study introduces an interpretable clustering approach for safety climate analysis. This study compares 5 algorithms for clustering truck drivers based on their safety climate perceptions. It proposes a novel method for quantitatively evaluating partial dependence plots (QPDP). To better interpret the clustering results, this study introduces different interpretable machine learning measures (SHAP, PFI, and QPDP). Drawing on data collected from more than 7,000 American truck drivers, this study significantly contributes to the scientific literature. It highlights the critical role of supervisory care promotion in distinguishing various driver groups. The Python code is available at https://github.com/NUS-DBE/truck-driver-safety-climate.

The sun had yet to rise in Delray Beach, Fla., when Jeremy Banner flicked on Autopilot. His red Tesla Model 3 sped down the highway at nearly 70 mph, his hands no longer detected on the wheel. Seconds later, the Tesla plowed into a semi-truck, shearing off its roof as it slid under the truck's trailer. Banner was killed on impact. Banner's family sued after the gruesome 2019 collision, one of at least 10 active lawsuits involving Tesla's Autopilot, several of which are expected to go to court over the next year. Together, the cases could determine whether the driver is solely responsible when things go wrong in a vehicle guided by Autopilot -- or whether the software should also bear some of the blame.

California governor Gavin Newsom worked late last night, vetoing a law that would have banned self-driving trucks without a human aboard from state roads until the early 2030s. State lawmakers had voted through the law with wide margins, backed by unions that argued autonomous trucks are a safety risk and threaten jobs. The bill would have seen California, which in 2012 became the first state to clear a regulatory path for autonomous vehicles, turn against self-driving technology just as driverless taxis are starting to serve the public. Autonomous truck developers now hope the freight-heavy state--home to two of the largest US ports--will one day become a critical link in an autonomous trucking network spanning the US. Companies developing the technology say it will save freight shippers money by enabling trucks to run loads on highways 24 hours a day, and by eliminating the dangers of distracted human driving, which could bring down insurance costs.

Developing cars that can drive without a human is a unique challenge. There are many kinds of events that fully autonomous vehicles have to be prepared to handle in milliseconds, and mistakes can have serious consequences. Solving these problems requires innovation across a number of fields, such as AI and machine learning, advanced sensors, simulation software that can mimic real-world driving, and computing frameworks to evaluate the system's performance. In 2007, I joined the Urban Challenge that was run by the US Defense Advanced Research Project Agency (Darpa) to test and develop autonomous vehicles (AVs). I vividly remember the first moment our car, Junior, drove by itself in the parking lot using software I was working on just hours before.

Truck accident attorneys like to market their services by telling potential clients about how dangerous trucking is and how things are only getting worse each day. It is true that there are more trucks on the road and the industry is losing experienced drivers; however, emerging technologies are likely to have a significantly positive impact on truck safety. In particular, artificial intelligence will change the way operators drive their trucks, and tests show the use of this technology will greatly reduce truck accidents. In other words, an even better future for truck safety is just over the horizon. There are several ways that AI can help truck drivers.

It's no secret that the trucking industry is responsible for bringing most of our everyday products to our doorstep in America. From clothes to food to televisions and smartphones, over-the-road trucking is likely part of the delivery process along the way. In fact, the trucking industry brings in over $700 billion per year while transporting 72.5% of American freight. As technology and regulations change around trucking, the companies and drivers can also utilize tech in new and exciting ways. Specifically, knowing what weather and potential delays to look out for while they're on the road.

The coronavirus pandemic has ushered in a new era of workplace surveillance that will extend well beyond our current crisis. Companies are increasingly monitoring employees who work from home, citing worries about security concerns or the need to boost employee productivity. In Amazon warehouses and UPS delivery trucks, surveillance technologies are being built into workplace infrastructure to monitor workers' every move. In many industries, employers can easily access phone calls, texts, browser histories, emails, and even GPS locations with very little effort. These exploitative surveillance practices are rooted in a historical mistrust of workers, especially low-wage workers, that can arguably be traced back to slavery and the exploitative "scientific management" practices that emerged from it, as bosses became obsessed with tracking workers' every move to maximize productivity and profit. Earlier forms of surveillance, like in the 19th century when companies hired Pinkerton private detectives to spy on workers, required a lot of labor. But modern technological advancements mean that the cost of surveillance today is very low.

Instead, the truck drives itself, and veteran driver Roger Nordqvist is at the ready only in case of unexpected problems. Swedish truck maker Scania is not the only auto manufacturer developing autonomous vehicles, but it recently became the first in Europe to pilot them while delivering commercial goods. "We take their goods from point A, drive them to point B, fully autonomously," Peter Hafmar, head of autonomous solutions at Scania, tells AFP outside the company's transport lab in Sodertalje, south of Stockholm. In the pilot project, the self-driving truck is manoeuvring a stretch of some 300 kilometres (186 miles) between Sodertalje and Jonkoping in Sweden's south, delivering fast-food goods. From the outside, the vehicle looks almost like any other lorry, save for a rail on the roof packed with cameras and two sensors resembling bug antennae on the sides.

"Will AI take over jobs?" is a very controversial and interesting question that has been around for many years, and yet it will be questioned even more in the upcoming years, as artificial intelligence rapidly develops. Some people believe that AI will create more jobs than it destroys. They argue that as AI automates certain tasks, it will free up workers to do other, more creative or complex tasks. For instance, a bank teller whose job is automated by AI may be able to use their freed-up time to provide financial planning services to customers. Similarly, a manufacturing worker whose job is taken over by a robot may be able to move into maintenance or quality control. Others believe that AI will destroy more jobs than it creates.