Temporally Consistent Road Surface Profile Estimation Using Stereo Vision

Lee, Jeong-Kyun; Yoon, Kuk-Jin

doi:10.1109/tits.2018.2794342

Cited by 11 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The digital elevation map method proposed in reference [21] converted the 3D point cloud into a digital elevation map, and combined the quadratic road surface model to detect and represent different types of obstacles. Reference [22] first identified the free space to segment the drivable area, and then used the pixels in the free space to estimate the digital elevation map to improve the estimation accuracy of the road profile. Compared with the original image, the data size of the stixel and digital elevation map is greatly reduced, and the influence of outliers on the detection results is weakened.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

Lin

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Obstacle detection is the primary task of the Advanced Driving Assistance System (ADAS). However, it is very difficult to achieve accurate obstacle detection in complex traffic scenes. To this end, this paper proposes an obstacle detection method based on the local spatial features of point clouds. Firstly, the local spatial point cloud of a superpixel is obtained through stereo matching and the SLIC image segmentation algorithm. Then, the probability of the obstacle in the corresponding area is estimated from the spatial feature information of the local plane normal vector and the superpixel point-cloud height, respectively. Finally, the detection results of the two methods are input into the Bayesian framework in the form of probabilities for the final decision. In order to describe the traffic scene efficiently and accurately, the detection results are further transformed into a multi-layer stixel representation. We carried out experiments on the KITTI dataset and compared several obstacle detection methods. The experimental results indicate that the proposed method has advantages in terms of its Pixel-wise True Positive Rate (PTPR) and Pixel-wise False Positive Rate (PFPR), particularly in complex traffic scenes, such as uneven roads.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

Lin

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…If a road profile recognition procedure by cameras is considered in two steps, the first step is image processing and the elevation profile creation and the second step is the conversion of the obtained elevation data to the road profile. In the literature, studies have generally been conducted to obtain height data through image processing [22][23][24]. The information available on how to combine elevation data to create a road profile, effectively, is highly limited.…”

Section: Introductionmentioning

confidence: 99%

Vision based road profile estimation for preview-controlled vehicle suspension systems

Büyükköprü,

Uzunsoy,

Mouton

2024

Computers and Informatics

View full text Add to dashboard Cite

In this paper, a vision-based road profile estimation method was studied for the control of semi-active and active suspension systems. For the purpose, a monocular camera was used to collect data from the road tests to develop a logic to convert the camera measurements into the road profile data. For the generation of the road profile, alignment of the different sets of camera measurements and their coherence were expressed. Importance of the sensor and process noise removal were shown in recognition of the high frequency content of the road profile, which was a particular interest of the study. Additionally, a density-based clustering algorithm was taken into account to cluster the measured points vertically, to remove the process and sensor noise. The density-based clustering method reduced the noises and allowed detection of the high and low frequency contents of the road.

show abstract

“…Many authors proposed three-dimensional (3D) techniques to address the disadvantages of 2D graylevel image analysis methods [24]. They use stereo vision and image registration to acquire 3D characteristics of the pavement [25], [26]. This method exhibits the lowest cost/performance ratio.…”

Section: Related Workmentioning

confidence: 99%

Data Driven Analytics of Road Quality

Taleqani

Bridgelall

Hough

et al.

EPiC Series in Computing

View full text Add to dashboard Cite

There is a lack of research into the impact of road roughness on ride quality and route choice. The scarcity of ride roughness data for local and urban roads is likely one reason for the lack of such studies. Existing methods of obtaining ride roughness data are expensive and require expert practitioners and laborious data processing by trained personnel. Sensors in most current vehicles provide an alternative source for road roughness data. This study emulated the data needed from vehicle sensors by using the accelerometer and gyroscope of a smartphone. The authors used data collected from two different bus routes to classify segments of roads into objectively distinct roughness clusters. The output enables map service applications to suggest better routing options based on expected ride quality and also quantifies road roughness consistently to enable optimized maintenance planning and decision-making for roadway assets.

show abstract

Temporally Consistent Road Surface Profile Estimation Using Stereo Vision

Cited by 11 publications

References 26 publications

A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

Vision based road profile estimation for preview-controlled vehicle suspension systems

Data Driven Analytics of Road Quality

Contact Info

Product

Resources

About