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Vehicle specific robust traversability indices using roadmaps on 3D pointclouds

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Abstract

Roads undergo rapid deterioration due to various economic, social and natural reasons. The uncertainty and unavailability of road safety information has become a major issue in reliably transporting goods, movement of heavy machinery, transport of people and materials to/from disaster areas and in even giving plain guarantees for safe traversability of a road that is good on paper. In this paper, we propose a novel approach towards reducing this uncertainty. Instead of asking qualitative questions about whether a road network is generally traversable for a general class of vehicles, we propose a solution that gives precise quantitative answers as a distance modulated roadmap and confirms whether a particular segment of the road is traversable for a particular type of vehicle. In this paper, we extend on our previous work Khan et al. (2016) of traversability analysis by empirically proving that our framework is robust to shape and size of the roadmap graph. We show that obstacle size, count, and location can have varying effects on traversability of different vehicles. We also propose a new Road Safety Index (RSI) which is an extension of our previously proposed index, Road Traversability Index (RTI). RSI takes into account multiple disjoint narrow passages that belong to the same connected component of the graph. Lastly, we present application scenarios where our roadmap based traversability can be used to assess the safety of the road for a particular vehicle.

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Notes

  1. CARMERA (https://www.carmera.com) and Civil Maps (https://civilmaps.com)

  2. In this work, road safety refers to a measure of traversable collision free space on the road. A road with more free space for a vehicle is safer than a road with less space. Hence, if an obstacle is introduced, it would decrease road safety if it hinders the vehicle motion.

  3. Unique paths are paths in a graph that do not share common nodes. Maxflow/Mincut can be used to compute it.

  4. To incorporate dynamic obstacles, we will have to model the obstacle motion and predict its location based on the time when vehicle reach the obstacle.

  5. We do see slight variation of the graph from bell shape for \(20\%\) graph size in Fig. 12 and we believe it is because of the very sparse nature of the graph. It is also evident in the boxplot in Fig. 11 where variation in RTI for graph size of \(20\%\) is very high.

  6. By collision free it is meant that even if an obstacle is present on the road but it is not hindering the vehicle motion then it is still considered collision free space.

  7. Space and paths are used interchangeably because paths approximately represent traversable space.

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Authors and Affiliations

  1. Syed Babar Ali School of Science and Engineering, LUMS, Lahore, Pakistan

    Muhammad Mudassir Khan

  2. Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany

    Karsten Berns

  3. Syed Babar Ali School of Science and Engineering, LUMS, Lahore, Pakistan

    Abubakr Muhammad

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  1. Muhammad Mudassir Khan
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  2. Karsten Berns
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  3. Abubakr Muhammad
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Correspondence to Muhammad Mudassir Khan.

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Khan, M.M., Berns, K. & Muhammad, A. Vehicle specific robust traversability indices using roadmaps on 3D pointclouds. Int J Intell Robot Appl 4, 490–506 (2020). https://doi.org/10.1007/s41315-020-00148-x

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