Leveraging a newly released open dataset of Lane Keeping Assist (LKA) systems from production vehicles, this paper presents the first comprehensive empirical analysis of real-world LKA performance. Our study yields three key findings: (i) LKA failures can be systematically categorized into perception, planning, and control errors. We present representative examples of each failure mode through in-depth analysis of LKA-related CAN signals, enabling both justification of the failure mechanisms and diagnosis of when and where each module begins to degrade; (ii) LKA systems tend to follow a fixed lane-centering strategy, often resulting in outward drift that increases linearly with road curvature, whereas human drivers proactively steer slightly inward on similar curved segments; (iii) We provide the first statistical summary and distribution analysis of environmental and road conditions under LKA failures, identifying with statistical significance that faded lane markings, low pavement laneline contrast, and sharp curvature are the most dominant individual factors, along with critical combinations that substantially increase failure likelihood. Building on these insights, we propose a theoretical model that integrates road geometry, speed limits, and LKA steering capability to inform infrastructure design. Additionally, we develop a machine learning-based model to assess roadway readiness for LKA deployment, offering practical tools for safer infrastructure planning, especially in rural areas. This work highlights key limitations of current LKA systems and supports the advancement of safer and more reliable autonomous driving technologies.

Traditional autonomous driving systems often struggle with reasoning in complex, unexpected scenarios due to limited comprehension of spatial relationships. In response, this study introduces a Large Language Model (LLM)-based Autonomous Driving (AD) assistance system that integrates a vision adapter and an LLM reasoning module to enhance visual understanding and decision-making. The vision adapter, combining YOLOv4 and Vision Transformer (ViT), extracts comprehensive visual features, while GPT-4 enables human-like spatial reasoning and response generation. Experimental evaluations with 45 experienced drivers revealed that the system closely mirrors human performance in describing situations and moderately aligns with human decisions in generating appropriate responses.

Users of shared control systems change their behavior in the presence of assistance, which conflicts with assumpts about user behavior that some assistance methods make. In this paper, we propose an analysis technique to evaluate the user's experience with the assistive systems that bypasses required assumptions: we model the assistance as a dynamical system that can be analyzed using control theory techniques. We analyze the shared autonomy assistance algorithm and make several observations: we identify a problem with runaway goal confidence and propose a system adjustment to mitigate it, we demonstrate that the system inherently limits the possible actions available to the user, and we show that in a simplified setting, the effect of the assistance is to drive the system to the convex hull of the goals and, once there, add a layer of indirection between the user control and the system behavior. We conclude by discussing the possible uses of this analysis for the field.

We propose a novel methodology to define assistance systems that rely on information fusion to combine different sources of information while providing an assessment. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.

Manual assembly workers face increasing complexity in their work. Human-centered assistance systems could help, but object recognition as an enabling technology hinders sophisticated human-centered design of these systems. At the same time, activity recognition based on hand poses suffers from poor pose estimation in complex usage scenarios, such as wearing gloves. This paper presents a self-supervised pipeline for adapting hand pose estimation to specific use cases with minimal human interaction. This enables cheap and robust hand posebased activity recognition. The pipeline consists of a general machine learning model for hand pose estimation trained on a generalized dataset, spatial and temporal filtering to account for anatomical constraints of the hand, and a retraining step to improve the model. Different parameter combinations are evaluated on a publicly available and annotated dataset. The best parameter and model combination is then applied to unlabelled videos from a manual assembly scenario. The effectiveness of the pipeline is demonstrated by training an activity recognition as a downstream task in the manual assembly scenario.

Digital Twins (DTs) are virtual representations of physical objects or processes that can collect information from the real environment to represent, validate, and replicate the physical twin's present and future behavior. The DTs are becoming increasingly prevalent in a variety of fields, including manufacturing, automobiles, medicine, smart cities, and other related areas. In this paper, we presented a systematic reviews on DTs in the autonomous vehicular industry. We addressed DTs and their essential characteristics, emphasized on accurate data collection, real-time analytics, and efficient simulation capabilities, while highlighting their role in enhancing performance and reliability. Next, we explored the technical challenges and central technologies of DTs. We illustrated the comparison analysis of different methodologies that have been used for autonomous vehicles in smart cities. Finally, we addressed the application challenges and limitations of DTs in the autonomous vehicular industry.

The issue of road safety has been of critical importance globally, as road traffic accidents have caused significant harm to individuals and society. Road traffic accidents are estimated to be the cause of 1.35 million deaths annually, according to the World Health Organization[1], making them the eighth leading cause of death worldwide. Overtaking has been one of the key contributors to road accidents, particularly on highways and rural roads. To address this problem, a new overtaking assistance system was proposed in this research paper that provided clear and timely signals to vehicles behind the host vehicle. In recent years, advances in computer vision and sensing technologies have led to the development of intelligent transportation systems that could enhance driving safety and efficiency. In this paper, a real-time overtaking assistance system was proposed that used a combination of the You Only Look Once (YOLO) object detection algorithm and stereo vision techniques to accurately identify and locate vehicles in front of the driver and estimate their distance. Using this information, the vehicles behind the host vehicle were signaled by means of colored signals, helping drivers make informed decisions on the overtaking procedure. The system was developed and tested in real-world scenarios, and the results demonstrated its effectiveness in detecting vehicles and accurately calculating their distances. The details of the system design and testing will be presented in this research paper, and the potential benefits and limitations of the approach will also be discussed.

For much of the past decade, Elon Musk has regularly voiced concerns about artificial intelligence, worrying that the technology could advance so rapidly that it creates existential risks for humanity. Though seemingly unrelated to his job making electric vehicles and rockets, Musk's A.I. Cassandra act has helped cultivate his image as a Silicon Valley seer, tapping into the science-fiction fantasies that lurk beneath so much of startup culture. Now, with A.I. taking center stage in the Valley's endless carnival of hype, Musk has signed on to a letter urging a moratorium on advanced A.I. development until "we are confident that their effects will be positive and their risks will be manageable," seemingly cementing his image as a force for responsibility amid high technology run amok. Existential risks are central to Elon Musk's personal branding, with various Crichtonian scenarios underpinning his pitches for Tesla, SpaceX, and his computer-brain-interface company Neuralink. But not only are these companies' humanitarian "missions" empty marketing narratives with no real bearing on how they are run, Tesla has created the most immediate--and lethal--"A.I. risk" facing humanity right now, in the form of its driving automation.

Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate significantly longer natural-sounding suggestions, offering more advanced assistance opportunities. This study explores the trade-offs between sentence- vs. message-level suggestions for AI-mediated communication. We recruited 120 participants to act as staffers from legislators' offices who often need to respond to large volumes of constituent concerns. Participants were asked to reply to emails with different types of assistance. The results show that participants receiving message-level suggestions responded faster and were more satisfied with the experience, as they mainly edited the suggested drafts. In addition, the texts they wrote were evaluated as more helpful by others. In comparison, participants receiving sentence-level assistance retained a higher sense of agency, but took longer for the task as they needed to plan the flow of their responses and decide when to use suggestions. Our findings have implications for designing task-appropriate communication assistance systems.

Automakers are jumping into the field of advanced driver assistance systems (ADAS) with both feet, trying to stuff as many features into their new cars as they can. The Insurance Institute for Highway Safety, though, wanted to find out what consumers actually want. The survey shows that the majority of consumers are pretty conservative when it comes to ADAS systems. After surveying 1,000 drivers on three partially automated driving systems (lane centering, automated lane changing, and driver monitoring), the IIHS found that consumers prefer systems where they are more in control that have more safeguards. Although consumer interest in ADAS technologies is strong, they are suspicious the more hands-free the technologies become.