Robust Drivable Road Region Detection for Fixed-Route Autonomous Vehicles Using Map-Fusion Images

Cai, Yonghua; Li, Dachuan; Zhou, Xiaohua; Mou, Xingang

doi:10.3390/s18124158

Cited by 19 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, the road is represented by its boundaries [9,10] or regions [1,2,11]. Moreover, road lane [12,13,14] and drivable area [15,16] detection also attract much attention from researchers, which concern the ego lane and the obstacle-free region of the road, respectively. The learning-based methods usually outperform the model-based methods due to the developed segmentation techniques.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Wang

Yan

Zou

et al. 2019

Sensors

View full text Add to dashboard Cite

The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. To this end, we create a small but effective dataset based on the KITTI dataset named KITTI-OFRS (KITTI-occlusion-free road segmentation) dataset and propose a lightweight and efficient, fully convolutional neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road layout. In particular, the global context module is used to build up the down-sampling and joint context up-sampling block in our network, which promotes the performance of the network. Moreover, a spatially-weighted cross-entropy loss is designed to significantly increases the accuracy of this task. Extensive experiments on different datasets verify the effectiveness of the proposed approach, and comparisons with current excellent methods show that the proposed method outperforms the baseline models by obtaining a better trade-off between accuracy and runtime, which makes our approach is able to be applied to autonomous vehicles in real-time.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The model-based methods identify the road structure and road areas by shape [17,18] or appearance models [19]. The learning-based methods [3,6,7,16,20,21] classify the pixels in images as road and non-road, or road boundaries and non-road boundaries.…”

Section: Related Workmentioning

confidence: 99%

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Wang

Yan

Zou

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Effective road detection is challenging for self-driving cars [22]. In the process of autonomous driving, road recognition depends on GPS, sensors, and high-resolution digital map guidance [23].…”

Section: Introductionmentioning

confidence: 99%

Incomplete Road Information Imputation Using Parallel Interpolation to Enhance the Safety of Autonomous Driving

et al. 2020

View full text Add to dashboard Cite

Autonomous driving is the main application of Internet of Things (IoT) technology in the field of intelligent transportation. In autonomous driving, self-driving cars will avoid changing lanes in a short distance. When the self-driving car executes the follow-up instruction, the road smoothness in front of the car will affect the driving safety and comfort of the car. The real-time acquisition of road information in front of driving will help the self-driving car adjust driving behavior. However, other vehicles on the road will lead to the failure of Light Detection And Ranging (LiDAR) detectors to obtain complete road point cloud data. The incomplete road point cloud data need to be imputated to avoid potential misjudgements of the road conditions. Currently, little research work specifically focuses on imputating the incomplete road point cloud data that are caused by obstacle vehicles. In this paper, we propose a fast method to imputate the incomplete road point cloud data using a Graphics Processing Unit (GPU)-based parallel Inverse Distance Weighted (IDW) interpolation algorithm to enhance the safety of autonomous driving. To evaluate the performance of the proposed method, two groups of experiments are conducted. The experimental results indicate the following: (1) the known point cloud data within 5 meters around the obstacle vehicle are sufficient to guarantee the imputation accuracy; (2) when the weight parameter of the IDW interpolation is 4, the efficiency and accuracy of the imputation can be optimally balanced; and (3) it takes approximately 0.6 seconds to imputate the incomplete dataset consisting of 15 million points, while the imputation error is approximately 5 millimeters. The proposed method is capable of efficiently and effectively imputating the incomplete road point cloud data that are induced by obstacle vehicles and outperforms other interpolation algorithms and machine learning algorithms. INDEX TERMS IoT, self-driving car, path planning, data imputation, interpolation algorithm, GPU.

show abstract

“…Road topology reconstruction is a fundamental yet long-standing problem for remote sensing applications [ 1 , 2 , 3 ], thus receiving wide attention in the past decades. Complete road topological networks are widely used in many fields, such as traffic flow monitoring [ 4 ], self-driving technology [ 5 ], intelligent public transportation [ 6 ], navigation [ 7 ], road map construction [ 8 ], traffic incident detection [ 9 , 10 ], etc. However, most methods cannot produce satisfactory road networks, due to the complex spectral condition of road area.…”

Section: Introductionmentioning

confidence: 99%

Road Topology Refinement via a Multi-Conditional Generative Adversarial Network

Zhang

2019

Sensors

View full text Add to dashboard Cite

With the rapid development of intelligent transportation, there comes huge demands for high-precision road network maps. However, due to the complex road spectral performance, it is very challenging to extract road networks with complete topologies. Based on the topological networks produced by previous road extraction methods, in this paper, we propose a Multi-conditional Generative Adversarial Network (McGAN) to obtain complete road networks by refining the imperfect road topology. The proposed McGAN, which is composed of two discriminators and a generator, takes both original remote sensing image and the initial road network produced by existing road extraction methods as input. The first discriminator employs the original spectral information to instruct the reconstruction, and the other discriminator aims to refine the road network topology. Such a structure makes the generator capable of receiving both spectral and topological information of the road region, thus producing more complete road networks compared with the initial road network. Three different datasets were used to compare McGan with several recent approaches, which showed that the proposed method significantly improved the precision and recall of the road networks, and also worked well for those road regions where previous methods could hardly obtain complete structures.

show abstract

Robust Drivable Road Region Detection for Fixed-Route Autonomous Vehicles Using Map-Fusion Images

Cited by 19 publications

References 28 publications

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Incomplete Road Information Imputation Using Parallel Interpolation to Enhance the Safety of Autonomous Driving

Road Topology Refinement via a Multi-Conditional Generative Adversarial Network

Contact Info

Product

Resources

About