How straylight affects driving ability Hershko, Sarah
Acta ophthalmologica (Oxford, England),
December 2022, 2022-12-00, 20221201, Letnik:
100, Številka:
S275
Journal Article
Recenzirano
We investigated how an increase in straylight affects the driving capacity of young, healthy volunteers between the ages of 20 and 40 years (i.e., people without cataract) in various real‐life ...driving circumstances using a driving simulator. This simulator allowed us to assess driving behaviour in a controlled, repeatable environment without risk to life or property, while providing a large set of parameters (speed, brake performance, deceleration, collisions, standard deviation of the lateral lane position, (SDLP), headway distance, etc.). The simulator used a fixed‐base setup with a force‐feedback steering wheel, an instrumented dashboard, brake, and accelerator pedals and with a 135° field of view. Participants had to drive along a certain course with of 6 traffic situations (e.g., crossing pedestrian, obstacle on the way, etc…) while wearing their own spectacle correction, both in the presence of a glare source and without. Next this was repeated while wearing a Tiffen Black Pro Mist (BPM) filter in front of their eyes and a glare source. These filters approximate the optical characteristics of cataract fairly well, where BPM 1 mimics simulates early cataract (which often prompts people to stop driving at night) and BPM 2 simulates serious straylight hindrance. Straylight was measured with the van den Berg straylight meter (C‐Quant).
The results showed that increased straylight significant alters driving behaviour, such as a decrease in mean, maximum and minimum speed. The detection time and reaction time to an obstacle on the road was significantly longer with increased straylight. Consequently, straylight is an important factor in traffic safety driving conditions, causing altered driving behaviour and increasing collision risk in certain traffic situations.
Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic ...driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude.
The realization of carbon neutral goal is inseparable from the development of new energy industry, and scientific and effective policy support can accelerate the progress of the goal. In this paper, ...the policy driven ability of China's photovoltaic industry in the background of carbon neutral is evaluated. Firstly, the evaluation system is established by the improved diamond model. Then, the policy evaluation standard is formulated according to the Interval Type-2 Fuzzy sets (IT2FS). Finally, the weight of each index is determined by using the fuzzy OWA operator weighting method (F-OWA). Then, the policy driving ability of China's photovoltaic industry is evaluated by fuzzy matter-element extension method (F-MEEM), and the effectiveness of the evaluation results is further verified by weight sensitivity analysis. According to the evaluation results, policy support plays an important role in the development of photovoltaic industry. This paper also gives policy suggestions for the development of China's photovoltaic industry under the background of carbon neutral from the macro, meso and micro perspectives.
•China's PV industry evaluation system is established by the improved diamond model.•IT2FS and F-OWA are used to process indicators.•The policy driving ability of China's PV industry is evaluated by F-MEEM.•The effectiveness of the evaluation results is further verified by weight sensitivity analysis.•The results are analyzed in detail, and corresponding suggestions are put forward.
Advances in autonomous driving provide an opportunity for AI-assisted driving instruction that directly addresses the critical need for human driving improvement. How should an AI instructor convey ...information to promote learning? In a pre-post experiment (n = 41), we tested the impact of an AI Coach’s explanatory communications modeled after performance driving expert instructions. Participants were divided into four (4) groups to assess two (2) dimensions of the AI coach’s explanations: information type (‘what’ and ‘why’-type explanations) and presentation modality (auditory and visual). We compare how different explanatory techniques impact driving performance, cognitive load, confidence, expertise, and trust via observational learning. Through interview, we delineate participant learning processes. Results show AI coaching can effectively teach performance driving skills to novices. We find the type and modality of information influences performance outcomes. Differences in how successfully participants learned are attributed to how information directs attention, mitigates uncertainty, and influences overload experienced by participants. Results suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications that can instruct without overwhelming. Further, results support the need to align communications with human learning and cognitive processes. We provide eight design implications for future autonomous vehicle HMI and AI coach design.
In this study, we experimentally identify the effect of liquid dielectrophoresis (LDEP) force on a superhydrophobic surface in directing the trajectory of moving water droplets across designed ...interdigitated electrodes and show that this method is capable of rapidly selecting droplets at a high speed (200 mm/s). As the droplets traverse down the surface by the electric field, their deflection on the edge of these electrodes is achieved successively, allowing for the selective manipulation of discrete droplets. A series of experiments were conducted to validate the relationships among droplet deflections, applied electric fields, and dynamic contact angles. Our findings reveal that the principal driving force behind the droplet deflections is the LDEP force, which can provide instant manipulation of moving droplets rather than a variation in contact angles brought about by electrowetting. This study presents a proof-of-concept experiment utilizing LDEP for high-throughput droplet selection and also highlights the potential applications of this mechanism in high-speed digital microfluidics (DMF) and biological separation methodologies.
This research proposes a framework for categorizing the radial tire mode shapes using machine learning (ML) based classification and feature recognition algorithms, advancing the development of a ...digital twin for tire performance analysis. Tire mode shape categorization is required to identify modal features in a specific frequency range to maximize driving performance and secure safety. However, the mode categorization work requires a lot of manual effort to interpret modes. Therefore, this study suggests an ML-based classification tool to replace the conventional categorization process with two primary objectives: (1) create a database by categorizing the tire mode shapes based on the identified features and (2) develop an ML-based surrogate model to classify the tire mode shapes without manual effort. The feature map of the tire mode shape is built with the Zernike annular moment descriptor (ZAMD). The mode shapes are categorized using the correlation value derived by the modal assurance criteria (MAC) with all ZAMD values for each tire mode shape and subsequently creating the appropriate labels. The decision tree, random forests, and XGBoost, the representative supervised-learning algorithms for classification, are implemented for surrogate model development. The best-performed classifier can categorize the mode shapes without any manual effort with a high accuracy of 99.5%.
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