VIAC: An out of ordinary experiment

Bertozzi, Massimo; Bombini, Luca; Broggi, Alberto; Buzzoni, Michele; Cardarelli, Elena; Cattani, Silvia; Cerri, Pietro; Coati, Alessandro; Debattisti, Stefano; Falzoni, Andrea; Fedriga, Rean Isabella; Felisa, Mirko; Gatti, Luca; Giacomazzo, Alessandro; Grisleri, Paolo; Laghi, Maria Chiara; Mazzei, Luca; Medici, Paolo; Panciroli, Matteo; Porta, Pier Paolo; Zani, Paolo; Versari, Pietro

doi:10.1109/ivs.2011.5940531

Cited by 54 publications

(29 citation statements)

References 6 publications

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“…A very large-scale experiment has been conducted by the VisLab team [255] with a 13,000-km trip from Italy to China. Technical details about the embedded technologies can be found in [256].…”

Section: Vehiclesmentioning

confidence: 99%

“…Based on their experience in DARPA, the authors clearly indicate that GPS and IMU are not sufficient for autonomous driving in urban environments. An evolution of [255] was presented in [258]. In a 13-km experiment in different environments and real traffic, VisLab demonstrated the possibility of using lane or leader following based on vision, IMU and DGPS.…”

Section: Vehiclesmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

628

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: Vehiclesmentioning

confidence: 99%

Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

628

366

View full text Add to dashboard Cite

show abstract

“…In order to reduce computational weight, a multi-threaded SIMD processing scheme has been devised, exploiting the parallel processing capabilities of the target hardware platform. The engine has been extensively tested during VIAC, the VisLab Intercontinental Autonomous Challenge [11], in a variety of scenarios and in different conditions.…”

Section: A 3d Points Enginementioning

confidence: 99%

A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision

Broggi

Cattani

Patander

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Autonomous Ground Vehicles designed for dynamic environments require a reliable perception of the real world, in terms of obstacle presence, position and speed. In this paper we present a flexible technique to build, in real time, a dense voxel-based map from a 3D point cloud, able to: 1) discriminate between stationary and moving obstacles; 2) provide an approximation of the detected obstacle's absolute speed using the information of the vehicle's egomotion computed through a visual odometry approach. The point cloud is first sampled into a full 3D map based on voxels to preserve the tridimensional information; egomotion information allows computational efficiency in voxels creation; then voxels are processed using a flood fill approach to segment them into a clusters structure; finally, with the egomotion information, the obtained clusters are labeled as stationary or moving obstacles, and an estimation of their speed is provided.The algorithm runs in real time; it has been tested on one of VisLab's AGVs using a modified SGM-based stereo system as 3D data source.

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“…Great care was taken to ensure that the devices installed on the vehicles did not affect the vehicle performance: sensors, processing systems, and actuation devices were all powered by solar energy, in order not to drain anything from the vehicles standard batteries. This extensive test allowed a thorough test of the systems and technologies, including the robustness of the hardware solutions; the results of the project are summarized in [6]. VIAC was the opportunity to perform a first preliminary test of the Digital Driving paradigm, because during the challenge some systems and functionalities of the new architecture were tested: leader-follower, stop&go, vehicle detector, waypoint following, panoramic viewing functionality, ditch&berms detector, and lane and obstacles detector.…”

Section: Testingmentioning

confidence: 99%

“…Reduced cost and size are also mandatory to allow straightforward integration and thus commercialization in the automotive market [2], [3]. On the other hand for autonomous vehicles, systems cost and size are currently less important but they have to deal with all weather conditions, all terrain, and all scenarios [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Moving from analog to digital driving

Broggi

Debattisti

Panciroli

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-This paper presents a new vehicle guidance paradigm based on high level commands together with a new system architecture that can handle different levels of automation. First, an overview of different automation levels spanning from manual driving to fully autonomous driving is given, then a detailed description of the digital driving paradigm is also shown. This includes a detailed description of the maneuver level planner which is based on a new concept of planning based on checkpoints. Then, an overview of the new architecture is given, describing is key aspects. An analysis of the proposed paradigm, with the description of a preliminary tests in real scenarios concludes this paper.

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VIAC: An out of ordinary experiment

Cited by 54 publications

References 6 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision

Moving from analog to digital driving

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