Required Visibility Level for Reliable Object Detection during Nighttime Road Traffic in Non-Urban Areas

Erkan, Anıl; Hoffmann, David; Kreß, Nikolai; Vitkov, Tsoni; Kunst, Korbinian; Peier, Markus Alexander; Khanh, Tran Quoc

doi:10.3390/app13052964

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…It is a conventional element in meteorological observation. Low-visibility weather seriously affects the safe operation of aviation, transportation, and power systems, and threatens human health because of its association with undiluted pollutant particles and toxic impurities [2][3][4][5]. Accurate visibility forecasting can provide the public with better travel plans and effectively reduce property losses and human casualties [6].…”

Section: Introductionmentioning

confidence: 99%

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

He,

Wang,

Tang

et al. 2024

Atmosphere

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High-quality visibility forecasting benefits traffic transportation safety, public services, and tourism. For a more accurate forecast of the visibility in the Guizhou region of China, we constructed several visibility forecasting models via progressive refinements in different compositions of input observational variables and the adoption of the Unet architecture to perform hourly visibility forecasts with lead times ranging from 0 to 72 h over Guizhou, China. Three Unet-based visibility forecasting models were constructed according to different inputs of meteorological variables. The model training via multiple observational variables and visibility forecasts of a high-spatiotemporal-resolution numerical weather prediction model (China Meteorological Administration, Guangdong, CMA-GD) produced a higher threat score (TS), which led to substantial improvements for different thresholds of visibility compared to CMA-GD. However, the Unet-based models had a larger bias score (BS) than the CMA-GD model. By introducing the U2net architecture, there was a further improvement in the TS of the model by approximately a factor of two compared to the Unet model, along with a significant reduction in the BS, which enhanced the stability of the model forecast. In particular, the U2net-based model performed the best in terms of the TS below the visibility threshold of 200 m, with a more than eightfold increase over the CMA-GD model. Furthermore, the U2net-based model had some improvements in the TS, BS, and RMSE (root-mean-square error) compared to the LSTM_Attention model. The spatial distribution of the TS showed that the U2net-based model performed better at the model grid scale of 3 km than at the scale of individual weather stations. In summary, the visibility forecasting model based on the U2net algorithm, multiple observational variables, and visibility data from the CMA-GD model performed the best. The compositions of input observational variables were the key factor in improving the deep learning model’s forecasting capability, and these improvements could improve the value of forecasts and support the socioeconomic needs of sectors reliant on visibility forecasting.

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Section: Introductionmentioning

confidence: 99%

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

He,

Wang,

Tang

et al. 2024

Atmosphere

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“…Furthermore, with the availability of light sources such as LEDs and lasers, possibilities for optimizing headlight distributions in terms of homogeneity and intensity have changed drastically. Numerous investigations have occurred in recent years regarding the visibility requirements from advanced headlight systems and illumination requirements for visibility and visually guided behavior at night MÜLLER, ROSENHAHN, KANNA, ERKAN, REAGAN, FUNK, WALDNER, CENGIZ, WINTER [8][9][10][11][12][13][14][15][16][17][18] While MOISEL has proposed some initial optimizations [19], the present series of papers will go into further detail.…”

Section: Introductionmentioning

confidence: 99%

A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data Part 1: Idealized Baseline Distribution

Kobbert,

Erkan,

Bullough

et al. 2023

Applied Sciences

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In order to find optimized headlight distributions based on real traffic data, a three-step approach is chosen. Since the complete investigations are too extensive to fit into a single publication, this paper is the first in a series of three publications. Over three papers, a novel way to optimize automotive headlight distributions based on real-life traffic and eye-tracking data is presented, based on 119 test subjects who participated in over 15,000 km of driving, including recordings of gaze behavior, light data, detection distances, and other objects in traffic. In the present paper, a baseline headlight distribution is derived from a series of detection tests conducted under ideal conditions, with a total of three tests, each with 19–30 subjects, conducted within the same test environment. In the first test, the influence of low beam intensity on the detection of pedestrians on the sidewalk (5.0 m from the center of the driving lane) is investigated. In the second test, the influence of different high beam intensities was investigated for the same detection task. In the third test, the headlight distribution and intensity are kept constant at a representative high beam level, but the detection task is changed. In this test, the pedestrian detection target is placed along different detection angles, ranging from immediately adjacent to the road (2.5°) to 15.5 m away from the center of the driving lane (8.0°). As mentioned, all of these tests were conducted under ideal conditions, with the studies taking place on an airfield with a 1.2 km long straight road and normal road markings, but without oncoming traffic, tasks other than keeping the vehicle with cruise control within its lane, or other distracting objects present. The tests yielded two sets of data; the first is the intensity, based on the first two studies, needed to ensure sufficient intensity to detect objects under ideal conditions at distances needed for different driving speeds. The last test then uses these intensities and necessary variations in the required intensity to create an idealized, symmetric headlight distribution as a baseline for subsequent publications. Although the distribution applies only to passenger vehicles like the one used in the test, the same approach could be applied to other vehicle types. The second paper of this series will focus on real traffic objects and their distributions within the traffic space in order to identify relevant areas in headlight distribution when driving under real traffic conditions. The third paper of this series will analyze driver gaze distributions during real driving scenarios. The data from all three papers are used to create optimized headlight distributions, thereby showing how such an optimized distribution relates to current headlight distributions in terms of luminous flux, intensity, and overall distribution.

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“…Since the science of tracking drivers' gaze behaviour has been evolving since at least 1972 [2], much work has been published over the last 50 years. In recent years, there have been numerous investigations into visibility from advanced headlighting systems and on the illumination requirements for visually guided behaviour [3][4][5][6][7][8][9][10][11][12][13]. Many different topics have been investigated such as the difference of gaze orientation between experienced and inexperienced drivers [2], orientation behaviour when cornering [14], and gaze directions of tired versus vigilant drivers [15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Way of Optimizing Headlight Distributions Based on Real-Life Traffic and Eye Tracking Data Part 3: Driver Gaze Behaviour on Real Roads and Optimized Light Distribution

Kobbert,

Erkan,

Bullough

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

In order to find optimized headlight distributions based on real traffic data, a three-step approach has been chosen. The complete investigations are too extensive to fit into a single paper; this paper is the last of a three part series. Over the three papers, a novel way to optimize automotive headlight distributions based on real-life traffic and eye tracking data is presented. Across all three papers, a total of 119 test subjects participated in the studies with over 15,000 km of driving, including recordings of gaze behaviour, light data, detection distances, and other objects in traffic. In this third paper, driver gaze behaviour is recorded while driving a 128 km round course, covering urban roads, country roads, and motorways. This gaze behaviour is then analysed and compared to prior work covering driver gaze behaviour. Comparing the gaze distributions with roadway object distributions from part two of this series, Analysis of Real-World Traffic Data in Germany and combining them with the idealized baseline headlight distribution from part one, different optimized headlight distributions can be generated. These headlight distributions can be optimized for different driving requirements based on the data that is used and weighting the different road types differently. The resulting headlight distribution is then compared to a standard light distribution in terms of the required luminous flux, angular distribution, and overall shape. Nonetheless, it is the overall approach that has been taken that we see as the primary novel outcome of this investigation, even more than the actual distribution resulting from this effort.

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Required Visibility Level for Reliable Object Detection during Nighttime Road Traffic in Non-Urban Areas

Cited by 4 publications

References 31 publications

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data Part 1: Idealized Baseline Distribution

A Novel Way of Optimizing Headlight Distributions Based on Real-Life Traffic and Eye Tracking Data Part 3: Driver Gaze Behaviour on Real Roads and Optimized Light Distribution

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