Comprehensive performance analysis of road detection algorithms using the common urban Kitti-road benchmark

Vitor, Giovani Bernardes; Victorino, Alessandro Corrêa; Ferreira, Janito Vaqueiro

doi:10.1109/ivs.2014.6856616

Cited by 17 publications

(13 citation statements)

References 23 publications

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“…In both [115,116], the classifier, which used a joint boosting algorithm, incorporated the feature maps based on textons (filter-bank, color, HoG and location) and disptons (U-disptons and V-disptons).…”

Section: Road Surface Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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Section: Road Surface Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…Machine learning methods are commonly used for this task. Some examples of those techniques are the Support Vector Machine (SVM) [32], Neural Networks (NN) [33], Bayes Classifier, decision trees (DT), Random Trees (RT), Extremely Randomized Trees (ERT), and boosting [34,35]. They receive a feature vector and the corresponding label for each pixel in the image.…”

Section: Featuresmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

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A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

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“…At the same time, other similar methods in KITTI-road are also compared for reference including HistonBoost, 46 BL, 4 RES3D-Stereo, 17 and BM. 47 In order to compare a learning-based method, a subpart of the method in equation (20) called ColorBoost is used as an example.…”

Section: Baselinementioning

confidence: 99%

“…In order to evaluate the performance of the proposed algorithm (ours is short for Geo þ GPR þ CRF and ours þ color for Geo þ Colorþ GPR þ CRF), a set of comparisons on KITTI-road data set are taken including HistonBoost, 46 BL, 4 RES3D-Stereo, 17 BM, 47 and ColorBoost.…”

Section: Multifeatþcrf Versus Multifeatþgprþcrfmentioning

confidence: 99%

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Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion

Xiao

Dai

et al. 2017

International Journal of Advanced Robotic Systems

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Free space detection is crucial to autonomous vehicles while existing works are not entirely satisfactory. As cameras have many advantages on environment perception, a stereo vision-based robust free space detection method is proposed which mainly depends on geometry information and Gaussian process regression. In this work, in order to improve the performance by exploiting multiple source information, we apply Bayesian framework and conditional random field inference to fuse the multimodal information including 2-D image and 3-D point geometric information. Particularly, a Bayesian framework is used for multiple feature fusion to provide a normalized and flexible output. Gaussian process regression is used to automatically and incrementally regress the data, resulting enhanced performance. Finally, conditional random field with color and geometry constrains is applied to make the result more robust. In order to evaluate the proposed method, quantitative experiments on popular KITTI-road data set and qualitative experiments on our own campus data set are tested. The results show satisfactory and inspiring performance compared to the outstanding works and even are competitive to some relevant Lidar-based methods.

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Comprehensive performance analysis of road detection algorithms using the common urban Kitti-road benchmark

Cited by 17 publications

References 23 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

A Hybrid Vision-Map Method for Urban Road Detection

Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion

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