A cooperative fusion architecture for robust localization: Application to autonomous driving

Bresson, Guillaume; Rahal, Mohamed-Cherif; Gruyer, Dominique; Revilloud, Marc; Alsayed, Zayed

doi:10.1109/itsc.2016.7795656

Cited by 22 publications

(14 citation statements)

References 11 publications

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“…using laser scanners and occupancy grids. This approach has been proven to be viable for autonomous driving in [153] in certain areas. The map can also be continuously updated but no dedicated process handles it, meaning that temporary changes will also be integrated.…”

Section: B Localization In a Previously Built Mapmentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

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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: B Localization In a Previously Built Mapmentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

Self Cite

666

366

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“…The examples of multi-sensor fusion have been widely recognized and applied in the research of intelligent vehicles [42]. You et al [43] combined the vehicle sensor information to carry out research on environmental information collection and analysis, trajectory generation, collision detection and conflict processing during the lane change processes.…”

Section: Research On Traffic Conflict Based On Multi-sensor Fusionmentioning

confidence: 99%

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Huang

et al. 2020

IEEE Access

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The traffic situation at an urban intersection is complicated, due to the numerous internal conflicts and all kinds of traffic accidents. With the objective of improving road safety, the automotive industry is moving toward intelligent vehicles. The major challenges are accurately perceiving the road traffic environment, detecting the potential traffic conflicts, and proposing the alternative driving strategies. This paper summarizes the existing researches on traffic conflicts from the perspective of intelligent vehicles. The intelligent vehicles can perceive the surrounding environment, extract road condition information, and detect obstacles for avoiding collisions or mitigating accidents. It expounds that the perception technology of intelligent vehicles can be divided into three main categories, namely, the perception technology, the communication technology, and the fusion of perception-communication technology. At the same time, the existing technical problems are analyzed. Finally, future development trends in this field are discussed. INDEX TERMS Intelligent vehicles, traffic conflicts, V2X communications, environment perception. I. INTRODUCTION

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“…Reducing the cost of sensing technologies while maintaining an efficient output is one of the priorities in AV systems, hence combining low-cost IMU data and GPS signals can yield continuous and accurate state estimations of vehicles [ 28 ]. Moreover, cameras and LiDAR [ 29 , 30 ] are used in a configuration that will allow extraction of specific environment primitives (road markings and static interest points) for use in either map building through simultaneous localization and mapping algorithms (SLAM) [ 31 ] or by matching them with a pre-existing high-definition (HD) map [ 32 ] and then obtaining accurate positions for both the ego vehicle and surrounding objects.…”

Section: Sensor Technology and Sensor Fusion Overviewmentioning

confidence: 99%

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Fayyad

Jaradat

Gruyer

et al. 2020

Sensors

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293

130

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Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

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A cooperative fusion architecture for robust localization: Application to autonomous driving

Cited by 22 publications

References 11 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 Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

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