Provident Detection of Vehicles at Night

Oldenziel, Emilio; Ohnemus, Lars; Saralajew, Sascha

doi:10.1109/iv47402.2020.9304752

Cited by 13 publications

(30 citation statements)

References 18 publications

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“…The provident detection of objects was already studied by several authors. At daylight, Naser et al [19] providently detected objects by analyzing shadow movements and, at nighttime, Oldenziel et al [8] and Saralajew et al [7] studied the task to providently detect oncoming vehicles by detecting light artifacts produced by the headlights. In particular, this work extends the analysis of the latter two and deploys the method in a test vehicle to providently control the car's matrix beam headlights.…”

Section: Related Workmentioning

confidence: 99%

“…Oldenziel et al [8] analyzed the discrepancy between the human abilities and an in-production computer vision system in detecting oncoming vehicles. Notably, based on the results of a test group study, the authors specified the deficit in detecting oncoming vehicles providently by 1.7 s on average in favor of humans.…”

Section: Related Workmentioning

confidence: 99%

“…Saralajew et al [7] extended the work of Oldenziel et al [8] and published a dataset containing 59 746 annotated gray-scale images for the task to providently detect oncoming vehicles at nighttime. In contrast to Oldenziel et al [8], the published dataset uses keypoint annotations with a clear annotation hierarchy to allow the investigation of several use cases. Moreover, with the keypoints as initial seeds, the authors further explored methods to extend the keypoint annotations to bounding boxes with low annotation uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…2: Visualization of the timings how an in-production system and humans (provident and after direct sight) perceive oncoming vehicles at night. The times are estimates from a test group study performed by Oldenziel et al [8] and are (of course) dependent on the scenario. However, they illustrate the inherent discrepancy.…”

Section: Inherent Limitation Of Current Systemsmentioning

confidence: 99%

“…Oldenziel et al [8] analyzed this discrepancy between the human detection capabilities and an in-production computer vision system and quantified that humans are approximately 1.7 s faster. One reason why stateof-the-art object detection systems are behind human capabilities is that object detection systems rely on the assumption that objects have clear, visible object boundaries.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

Ewecker¹,

Asan²,

Ohnemus³

et al. 2021

Preprint

Self Cite

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In recent years, computer vision algorithms have become more and more powerful, which enabled technologies such as autonomous driving to evolve with rapid pace. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a major drawback compared to human behavior, where indirect visual cues caused by the actual object (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by oncoming vehicles to assume their future appearance, whereas current object detection systems rely on the oncoming vehicle's direct visibility. Based on previous work in this subject, we present with this paper a complete system capable of solving the task to providently detect oncoming vehicles at nighttime based on their caused light artifacts. For that, we outline the full algorithm architecture ranging from the detection of light artifacts in the image space, localizing the objects in the three-dimensional space, and verifying the objects over time. To demonstrate the applicability, we deploy the system in a test vehicle and use the information of providently detected vehicles to control the glare-free high beam system proactively. Using this experimental

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Inherent Limitation Of Current Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

Ewecker¹,

Asan²,

Ohnemus³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Provident vehicle detection at night for advanced driver assistance systems

Ewecker¹,

Asan

Ohnemus

et al. 2022

Auton Robot

Self Cite

View full text Add to dashboard Cite

In recent years, computer vision algorithms have become more powerful, which enabled technologies such as autonomous driving to evolve rapidly. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently (denoted as provident vehicle detection). For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively (react before the oncoming vehicle is directly visible). Using this experimental setting, the provident vehicle detection system’s time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently (detection based on observable visual cues the objects cause before they are visible) and further close the performance gap between human behavior and computer vision algorithms to bring autonomous and automated driving a step forward.

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Analysis of Automobile Wheel Counting using Novel adaboosting Algorithm with Accuracy Compared to Logistic Regression Algorithm

Manikanta

Logu

2023

E3S Web Conf.

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Aim: In order to determine the accuracy of a realtime traffic management system, this work compares novel adaboosting and logistic regression methods to forecast the AutoMobile Wheel Movement Counting. Materials and Methods: The dataset utilized in this article contains 12 columns or attributes and a total of 10,684 rows. The columns in the dataset are named Car Wheels, Bicycle Wheels, Motorcycle Wheels, and Truck Wheels. The data source link provided a sample size of 1,340 records. A Novel adaboosting algorithm (N=20) and Logistic regression (N=20) iterations are simulated by various parameters and automate vehicle monitoring systems to optimize the pH. The 40 iterations were calculated using CilnCal with G power 80% and CI of 95%. Results: Based on obtained results Novel adaboosting Algorithm has significantly better accuracy (84.71%) compared to Logistic regression Algorithm accuracy (80.60%). Statistical significance difference between Novel adaboosting and Logistic regression algorithm was found to be p=0.013 (Independent Sample T Test p<0.05). Conclusion: Novel adaboosting algorithms provide better results in Finding Road Traffic counting than Logistic regression algorithms.

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Provident Detection of Vehicles at Night

Cited by 13 publications

References 18 publications

Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

Provident Vehicle Detection at Night for Advanced Driver Assistance Systems

Provident vehicle detection at night for advanced driver assistance systems

Analysis of Automobile Wheel Counting using Novel adaboosting Algorithm with Accuracy Compared to Logistic Regression Algorithm

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