Passive Vision Region-Based Road Detection

Rateke, Thiago; Justen, Karla Aparecida; Chiarella, Vito F.; Sobieranski, Antonio Carlos; Comunello, Eros; Wangenheim, Aldo von

doi:10.1145/3311951

Cited by 20 publications

(18 citation statements)

References 79 publications

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“…It's common to find in the literature studies focused on path detection that identify the path on different surfaces, but which don't extract relevant road features, such as surface type and also if there is some damage on that surface. ( [2], [3], [4], [5], [6] and several other examples as shown in this Systematic Literature Review about road detection [7]).…”

Section: Introductionmentioning

confidence: 78%

“…In [7] it's possible to notice that there was a great advance in the use of Convolutional Neural Networks (CNN) together with Computer Vision (VC) to do the vehicular navigation tasks. In addition, the datasets available are mostly from developed countries where roads have good quality, no damage and/or no major variations in the road surface types.…”

Section: Introductionmentioning

confidence: 99%

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Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Rateke

Justen

Wangenheim

2019

RITA

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The type of road pavement directly influences the way vehicles are driven. It’s common to find papers that deal with path detection but don’t take into account major changes in road surface patterns. The quality of the road surface has a direct impact on the comfort and especially on the safety of road users. In emerging countries it’s common to find unpaved roads or roads with no maintenance. Unpaved or damaged roads also impact in higher fuel costs and vehicle maintenance. This kind of analysis can be useful for both road maintenance departments as well as for autonomous vehicle navigation systems to verify potential critical points. For the experiments accomplishment upon the surface types and quality classification, we present a new dataset, collected with a low-cost camera. This dataset has examples of good and bad asphalt (with potholes and other damages) other types of pavement and also many examples of unpaved roads (with and without potholes). We also provide several frames from our dataset manually sorted in surface types for tests accuracy verification. Our road type and quality classifier was done through a simple Convolutional Neural Network with few steps and presents promising results in different datasets.

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Section: Introductionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Rateke

Justen

Wangenheim

2019

RITA

Self Cite

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“…The construction of a lane-level road network relies on vision-based methods, which generally extract the lane lines of road geometry combined with GPS data. Vision-based methods for lane extraction have become a hot topic [72][73][74] in recent decades because of the low cost of vision equipment. These methods are mainly categorized into two types: feature-based and model-based methods [75].…”

Section: Vision-based Methodsmentioning

confidence: 99%

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Zheng

Yang

et al. 2019

Sustainability

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Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review.

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“…The steps to deduce the traffic data are mentioned in Algorithm 1. [7]. The results of Algorithm 1 are shown in Fig.…”

Section: Fig 2: Wsn Based Traffic Systems -Proposed Architecturementioning

confidence: 99%

Dynamic Control of Traffic Signals using Traffic Data from Google Maps and Road Cameras

2019

ijrte

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Traffic congestion is a condition that occurs due to over usage of roads and results in increased waiting time, slower speeds and increased vehicle queuing. Every year billions of dollars are lost due to time wasted by waiting at traffic signals. In this paper, the problem of dynamic control of traffic signals using traffic data obtained from Google Maps and Road Cameras is explored. The traffic signal control algorithm described will adjust both the ordering of different lights in real time as well as the duration of each of them. The ordering (sequence) and length of each traffic signal light for a period of time will be affected by factors such as traffic volume, waiting time and vehicle queuing characteristics. The results from the above algorithm’s simulations are compared to the average waiting times the vehicles experience in the case of static traffic signals which employ a fixed duration and sequence policy for each of the colors of the traffic signal.

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Passive Vision Region-Based Road Detection

Cited by 20 publications

References 79 publications

Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Dynamic Control of Traffic Signals using Traffic Data from Google Maps and Road Cameras

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