A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i>
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/17/9908 |
| _version_ | 1797582833224515584 |
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| author | Jonas Kobbert Anil Erkan John D. Bullough Tran Quoc Khanh |
| author_facet | Jonas Kobbert Anil Erkan John D. Bullough Tran Quoc Khanh |
| author_sort | Jonas Kobbert |
| collection | DOAJ |
| description | 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.0</mn></mrow></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="normal">m</mi></semantics></math></inline-formula> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.5</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>) to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>15.5</mn></mrow></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="normal">m</mi></semantics></math></inline-formula> away from the center of the driving lane (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>8.0</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>). As mentioned, all of these tests were conducted under ideal conditions, with the studies taking place on an airfield with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.2</mn></mrow></semantics></math></inline-formula> 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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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:28:10Z |
| publishDate | 2023-09-01 |
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| spelling | doaj.art-e6b55bac92fe41ba9faacfc2745d893a2023-11-19T07:53:22ZengMDPI AGApplied Sciences2076-34172023-09-011317990810.3390/app13179908A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i>Jonas Kobbert0Anil Erkan1John D. Bullough2Tran Quoc Khanh3AUDI AG, Auto-Union-Str. 1, 85057 Ingolstadt, GermanyLaboratory of Adaptive Lighting Systems and Visual Processing, Technical University of Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, GermanyIcahn School of Medicine at Mount Sinai, Light and Health Research Center, Population Health Science and Policy, 150 Broadway, Suite 560, Albany, NY 12204, USALaboratory of Adaptive Lighting Systems and Visual Processing, Technical University of Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, GermanyIn 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.0</mn></mrow></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="normal">m</mi></semantics></math></inline-formula> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.5</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>) to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>15.5</mn></mrow></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="normal">m</mi></semantics></math></inline-formula> away from the center of the driving lane (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>8.0</mn></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>). As mentioned, all of these tests were conducted under ideal conditions, with the studies taking place on an airfield with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.2</mn></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2076-3417/13/17/9908automotive lightingadaptive driving beamlight distributionseye trackinggaze distributionspedestrian |
| spellingShingle | Jonas Kobbert Anil Erkan John D. Bullough Tran Quoc Khanh A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i> Applied Sciences automotive lighting adaptive driving beam light distributions eye tracking gaze distributions pedestrian |
| title | A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i> |
| title_full | A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i> |
| title_fullStr | A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i> |
| title_full_unstemmed | A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i> |
| title_short | A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data <i>Part 1: Idealized Baseline Distribution</i> |
| title_sort | novel way of optimizing headlight distributions based on real life traffic and eye tracking data i part 1 idealized baseline distribution i |
| topic | automotive lighting adaptive driving beam light distributions eye tracking gaze distributions pedestrian |
| url | https://www.mdpi.com/2076-3417/13/17/9908 |
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