Robust localization using a top-down approach with several LIDAR sensors

Delobel, Laurent; Aynaud, Claude; Aufrère, Romuald; Debain, Christophe; Chapuis, Roland; Chateau, Thierry; Bernay-Angeletti, Coralie

doi:10.1109/robio.2015.7419693

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Instead of lanes, the authors of [191] use a map containing the walls of the surroundings. A Bayesian network decides the most appropriate wall to detect using a laser scanner in order to reach a defined accuracy objective in a top-down manner.…”

Section: Localization In Existing Mapsmentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

705

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: Localization In Existing Mapsmentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

705

366

View full text Add to dashboard Cite

show abstract

“…Compared to camera and radar, LiDAR offers much higher accuracy on detecting the position, 3D geometry, speed, and moving direction of obstacles [33,50]. Therefore, most of current Level-4 AVs highly rely on LiDAR to perform the perception task [26,31,35]. LiDAR continues to emit lasers across a 360 degree view.…”

Section: Tasks Of Av Computing Systemmentioning

confidence: 99%

Driving Scenario Perception-Aware Computing System Design in Autonomous Vehicles

Zhao

Zhang²,

Meng³

et al. 2020

2020 IEEE 38th International Conference on Computer Design (ICCD)

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Recently, autonomous driving development ignited competition among car makers and technical corporations. Low-level automation cars are already commercially available. But high automated vehicles where the vehicle drives by itself without human monitoring is still at infancy. Such autonomous vehicles (AVs) rely on the computing system in the car to to interpret the environment and make driving decisions. Therefore, computing system design is essential particularly in enhancing the attainment of driving safety. However, to our knowledge, no clear guideline exists so far regarding safetyaware AV computing system and architecture design. To understand the safety requirement of AV computing system, we performed a field study by running industrial Level-4 autonomous driving fleets in various locations, road conditions, and traffic patterns. The field study indicates that traditional computing system performance metrics, such as tail latency, average latency, maximum latency, and timeout, cannot fully satisfy the safety requirement for AV computing system design. To address this issue, we propose a "safety score" as a primary metric for measuring the level of safety in AV computing system design. Furthermore, we propose a perception latency model, which helps architects estimate the safety score of given architecture and system design without physically testing them in an AV. We demonstrate the use of our safety score and latency model, by developing and evaluating a safety-aware AV computing system computation hardware resource management scheme.

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Multi-vehicles localization by using a georeferenced map and a top-down approach for automatic guidance

Aufrère

Delobel

Alizon

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Robust localization using a top-down approach with several LIDAR sensors

Abstract: International audienc

Cited by 6 publications

References 12 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

Driving Scenario Perception-Aware Computing System Design in Autonomous Vehicles

Multi-vehicles localization by using a georeferenced map and a top-down approach for automatic guidance

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