Recognition of Highway Workzones for Reliable Autonomous Driving

Seo, Yongsok; Lee, Jong-Ho; Zhang, Wende; Wettergreen, David

doi:10.1109/tits.2014.2335535

Cited by 24 publications

(12 citation statements)

References 33 publications

(53 reference statements)

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“…Therefore, the boundary of road construction needs to be accurately identified and potential hazards there need to be specifically handled. To this end, [20] focuses on identifying work zone boundary and driving condition change on highways. [22] reviewing recent progress, very few studies focus on robustness of these methods.…”

Section: Driveability Factorsmentioning

confidence: 99%

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo¹,

Kurup²,

Shah³

2020

IEEE Trans. Intell. Transport. Syst.

129

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With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 45 public driving datasets that enable learning and publish this dataset index at https://sites.google.com/view/driveability-surveydatasets. Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.

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Section: Driveability Factorsmentioning

confidence: 99%

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo¹,

Kurup²,

Shah³

2020

IEEE Trans. Intell. Transport. Syst.

129

View full text Add to dashboard Cite

show abstract

“…and safety warning systems [1], [2], autonomous driving [3]- [5], and traffic flow monitoring and prediction [6]- [8]. In addition, accurate, real-time information regarding current road conditions, traffic flow, and the surrounding environment is of great significance and necessity to the Intelligent Transportation Systems.…”

mentioning

confidence: 99%

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Guan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a novel algorithm for detecting road scene objects (e.g., light poles, traffic signposts, and cars) from 3-D mobile-laser-scanning point cloud data for transportationrelated applications. To describe local abstract features of point cloud objects, a contextual visual vocabulary is generated by integrating spatial contextual information of feature regions. Objects of interest are detected based on the similarity measures of the bag of contextual-visual words between the query object and the segmented semantic objects. Quantitative evaluations on two selected data sets show that the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.949, 0.970, 0.922, and 0.959, respectively, in detecting light poles, traffic signposts, and cars. Comparative studies demonstrate the superior performance of the proposed algorithm over other existing methods.

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“…Overett et al [6] propose a LiteHOG+ descriptor that extended the HOG feature [7] by a centre-surround statistics, and classify with a cascade FDA classifier. Seo et al [8] present a color-based feature with AdaBoost classifier to detect the signs. Besides, they construct a log-polar transform and the SVM method to classify the signs.…”

Section: Introductionmentioning

confidence: 99%

Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

Weng¹,

Chiu²

2018

JTTs

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Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the "merge-stack" operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm 2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.

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Recognition of Highway Workzones for Reliable Autonomous Driving

Cited by 24 publications

References 33 publications

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

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