1,721,089 research outputs found
Pavement distress detection and avoidance for intelligent vehicles
No full textPavement distresses and potholes represent road hazards that can cause accidents and damages to vehicles. The latter may vary from a simple flat tyre to serious failures of the suspension system, and in extreme cases to collisions with third-party vehicles and even endanger passengers' lives. The primary scientific aim of this study is to investigate the problem of road hazard detection for driving assistance purposes, towards the final goal of implementing such a technology on future intelligent vehicles. The proposed approach uses a depth sensor to generate an environment representation in terms of 3D point cloud that is then processed by a normal vector-based analysis and presented to the driver in the form of a traversability grid. Even small irregularities of the road surface can be successfully detected. This information can be used either to implement driver warning systems or to generate, using a cost-to-go planning method, optimal trajectories towards safe regions of the carriageway. The effectiveness of this approach is demonstrated on real road data acquired during an experimental campaign. Normal analysis and path generation are performed in post-analysis. This approach has been demonstrated to be promising and may help to drastically reduce fatal traffic casualties, as a high percentage of road accidents are related to pavement distress
Road Surface Analysis for Driving Assistance
No full textIn order to increase the level of driving automation in future
cars, it is important to address critical issues, including road monitoring
for irregularities and hazard detection. In this concern, the primary sci-
entific aim of this research is to investigate the problem of road surface
analysis in urban and extra-urban scenarios for driving assistance pur-
poses towards the final goal of implementing such technologies on future
driverless cars. The proposed approach uses a range sensor to generate
an environment representation in terms of 3D point cloud that is then
processed by a normal vector-based analysis. Even small irregularities of
the road surface can be successfully detected, using such information to
warn the driver or enable an autonomous vehicle to regulate its speed
and change its course appropriately
A kinect-based parking assistance system
No full textThis work presents an IR-based system for parking assistance and obstacle detection in the automotive field that employs the Microsoft Kinect camera for fast 3D point cloud reconstruction. In contrast to previous research that attempts to explicitly identify obstacles, the proposed system aims to detect “reachable regions” of the environment, i.e., those regions where the vehicle can drive to from its current position. A user-friendly 2D traversability grid of cells is generated and used as a visual aid for parking assistance. Given a raw 3D point cloud, first each point is mapped into individual cells, then, the elevation information is used within a graph-based algorithm to label a given cell as traversable or non-traversable. Following this rationale, positive and negative obstacles, as well as unknown regions can be implicitly detected. Additionally, no flat-world assumption is required. Experimental results, obtained from the system in typical parking scenarios, are presented showing its effectiveness for scene interpretation and detection of several types of obstacle
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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