The Impact of Cooperative Perception on Decision Making and Planning of Autonomous Vehicles

Kim, Seong-Woo; Liu, Wei; Ang, Marcelo H.; Frazzoli, Emilio; Rus, Daniela

doi:10.1109/mits.2015.2409883

Cited by 105 publications

(54 citation statements)

References 38 publications

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“…Similar ideas have been applied for sensing and reusing available spectrum for cognitive radio in wireless communications systems [140], [141], [142]. Cooperative perception among multiple vehicles [58] improves situation awareness and perception capability, and provides traffic information beyond line-ofsight and field-of-view. It can be useful for situations such as hidden obstacle avoidance, safe lane-changing/overtaking, smooth braking/acceleration, etc.…”

Section: Vehicle As a Servermentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

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Section: Vehicle As a Servermentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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“…Authors in [22] utilize information from multiple sources through increased vehicle communication capabilities (vehicle-to-vehicle and vehicle-to-infrastructure) in order to provide a more efficient driving style. Researchers in [23] aim at increasing vehicle's perception by cooperatively utilizing information from other vehicles or infrastructure, through wireless connectivity. Based on that enhanced perception, the car can provide augmented situational awareness for a safer and more efficient vehicular experience for the passenger.…”

Section: Related Workmentioning

confidence: 99%

Environmental-Based Speed Recommendation for Future Smart Cars

et al. 2019

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Modern vehicles are enhanced with increased computation, communication and sensing capabilities, providing a variety of new features that pave the way for the deployment of more sophisticated services. Specifically, smart cars employ hundreds of sensors and electronic systems in order to obtain situational and environmental information. This rapid growth of on-vehicle multi-sensor inputs along with off-vehicle data streams introduce the smart car era. Thus, systematic techniques for combining information provided by on- and off-vehicle car connectivity are of remarkable importance for the availability and robustness of the overall system. This paper presents a new method to employ service oriented agents that cohesively align on- and off-vehicle information in order to estimate the current status of the car. In particular, this work combines, integrates, and evaluates multiple information sources targeting future smart cars. Specifically, the proposed methodology leverages weather-based, on-route, and on-vehicle information. As a use case, the presented work informs the driver about the recommended speed that the car should adapt to, based on the current status of the car. It also validates the proposed speed with real-time vehicular measurements.

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“…In a common traffic scenario with multiple vehicles, communication among the vehicles could result in an increased situational awareness about the environment. This wireless information sharing among vehicles is proposed to be implemented using Dedicated Short Range Communication Equipment(DSRC) [4] or through cellular communication technology [7] across a local area. This form of information sharing can happen among the vehicles (V2V), as well as information could be exchanged with sensors placed on infrastructure adjoining the roads (V2I).…”

Section: Chapter Introduction 11 Motivation and Backgroundmentioning

confidence: 99%

Cooperative perception in autonomous ground vehicles using a mobile‐robot testbed

Sridhar

Eskandarian

2019

IET Intelligent Transport Systems

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With connected and autonomous vehicles, no optimal standard or framework currently exists, outlining the right level of information sharing for cooperative autonomous driving. Cooperative Perception is proposed among vehicles, where every vehicle is transformed into a moving sensor platform that is capable of sharing information collected using its on-board sensors. This helps extend the line of sight and field of view of autonomous vehicles, which otherwise suffer from blind spots and occlusions. This increase in situational awareness promotes safe driving over a short range and improves traffic flow efficiency over a long range. I'm deeply indebted to my graduate advisor and mentor, Dr.Azim Eskandarian for having provided me, the opportunity to work with him and learn from his years of experience on Intelligent Vehicles Research. I'm grateful for all the insight, both technical and on the general nature of research, and for his constant motivation, patience and optimism, which kept me going. He steered me in the right direction when necessary, but also provided the freedom that empowered me to develop independent ideas. I thank Dr.John Ferris and Dr.Saied Taheri for serving as my graduate thesis committee members, for their expert feedback and for reviewing this work to completeness. I am also indebted to Dr.John Ferris for his guidance and advice during my time at Virginia Tech. I'm thankful to all my labmates at the ASIM Lab, especially Dr.Yuan Lin for his contribution in developing the initial indoor test-track and positioning system and Dr.Lirong Wang for her feedback and advice. I'm grateful to my friends and colleagues for all the motivation, technical insight and support in much needed moments. I thank Savio Pereira for the detailed discussions that helped refine the idea. I thank my professors at Virginia Tech for equipping me with the necessary background for carrying out this research. I'm also thankful to the ME Department Staff, who always had the time and a solution, for all my queries, that helped complete this project on time. Lastly, I'm grateful to my parents for having believed in me. None of this would have seen the light of day, if not for them. They were a source of endless emotional support and their love, assurance and motivation kept me working through thick and thin and succeed in Graduate school.

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The Impact of Cooperative Perception on Decision Making and Planning of Autonomous Vehicles

Cited by 105 publications

References 38 publications

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Environmental-Based Speed Recommendation for Future Smart Cars

Cooperative perception in autonomous ground vehicles using a mobile‐robot testbed

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