CRF based road detection with multi-sensor fusion

Xiao, Liang; Dai, Bin; Liu, Daxue; Hu, T. C.; Wu, Tao

doi:10.1109/ivs.2015.7225685

Cited by 98 publications

(70 citation statements)

References 28 publications

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“…Xiao et al also proposed a random field approach in [136] for sensor fusion but with different energy formulation as compared to [135]. The energy function consists of three terms, out of which, two were the same as normal MRF terms (value term and smoothness term).…”

Section: Fusionmentioning

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: Fusionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…n m (20) where K m is the covariance matrix for the input c m in segment m and can be calculated by equation (11).…”

Section: Learning Geometric Hyperparametersmentioning

confidence: 99%

“…Similarly as the mapping in equation (20), the unary potential C i ðx i Þ takes the negative log cost energy dðx i Þ from Gaussian process regression as the form C i ðx i Þ ¼ À logðdðx i ÞÞ which is nontrivial because this mapping widens the margin between the two classes and benefits the CRF inference.…”

Section: Crf Fusionmentioning

confidence: 99%

“…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%

“…The algorithm in this work mainly focuses on geometry features, while in order to show that it is flexible to add new features to improve the performance, the color and context features trained by boosted trees in equation (20) are introduced in the fusion framework. The improvement by new feature is shown in Figure 12 where the color and context information makes the first sudden change on …”

Section: Multifeatþcrf Versus Multifeatþgprþcrfmentioning

confidence: 99%

See 2 more Smart Citations

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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Lane marking detection algorithm based on high‐precision map and multisensor fusion

Yao

Chen

Liu

et al. 2020

Concurrency and Computation

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Summary In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high‐precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter‐level high‐precision positioning combined with high‐precision map data to complete the detection of lane markings. In the process of generating high‐precision maps or in the uncovered areas of high‐precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust.

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CRF based road detection with multi-sensor fusion

Cited by 98 publications

References 28 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

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

Lane marking detection algorithm based on high‐precision map and multisensor fusion

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