Browse Topic: Research and development
Video analysis plays a major role in many forensic fields. Many articles, publications, and presentations have covered the importance and difficulty in properly establishing frame timing. In many cases, the analyst is given video files that do not contain native metadata. In other cases, the files contain video recordings of the surveillance playback monitor which eliminates all original metadata from the video recording. These “video of video” recordings prevent an analyst from determining frame timing using metadata from the original file. However, within many of these video files, timestamp information is visually imprinted onto each frame. Analyses that rely on timing of events captured in video may benefit from these imprinted timestamps, but for forensic purposes, it is important to establish the accuracy and reliability of these timestamps. The purpose of this research is to examine the accuracy of these timestamps and to establish if they can be used to determine the timing
The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the
The study analyzed data from on-road drives with a pre-production Level 2 (L2) partial automation system using a sample of 27 drivers ranging from 21 to 75 years of age. The system provides continuous automatic lateral and longitudinal control but requires the driver to remain attentive and intervene when necessary. The L2 system was equipped with a Driving Monitoring System (DMS) that issued escalating alerts to remind the driver to pay attention or take over when needed. During the 14-month study period, drivers completed 354,768 miles of travel with the L2 system engaged, totaling 5,913 trips. The results of the study showed that drivers were highly responsive to attention reminders and takeover alerts, with high compliance rates and quick response times. Importantly, there was no evidence of habituation to these alerts over time. These findings support the effectiveness of the system's DMS and alert HMI (Human-Machine Interface) strategy in promoting the proper use of the system
Heat shrink polymer is a type of material used in many industries’ segments due to their ability to contract and fit snugly around objects when heat is applied. These products are commonly commercialized in tube format (e.g.: sleeves), made from polyolefin or fluoropolymers, which have the property of shrinking when heated. Nanomaterials present many applications, and their usage is a remarkable tool aiming to improve many properties of materials. Then, many improvements including increase of performance and price reduction may be achieved due to its unique properties when nanomaterials are used into heat shrink polymer sleeves. This work presents a systematic review about the state of the art on heat-shrinkable materials for the automotive industry. As a methodology, articles from the last 10 years on the subject were selected. The keywords “heat shrink” AND “nanomaterial” AND “tubes OR sleeves” were used in three different databases, being “Scopus”, “Web of Science” and “MDPI”. After
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
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