Evaluating the requirements of communicating vehicles in collaborative automated driving

Ozbilgin, Guchan; Özgüner, Ü.; Altintas, Onur; Kremo, Haris; Maroli, John M.

doi:10.1109/ivs.2016.7535521

Cited by 12 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, vehicle connectivity consists of different communication technologies 108 for vehicles, such as V2V, V2I, and V2P 60 . Multiple vehicular communication research [109][110][111][112][113] has been conducted to establish efficient and realistic cooperative communication systems. This work is dedicated to evaluating some existing reviews and journal papers in this phase as seen in Table 5.…”

Section: Communication and Cooperationmentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

show abstract

Section: Communication and Cooperationmentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Interest in intelligent vehicles research has been growing in the last two decades, which significantly improves transportation security and comfort (Ozbilgin et al 2016). In full collaborative autonomy, on-board sensors from individual cars and data sharing between connected vehicles are used in conjunction to increase the overall "intelligence" of traffic (Ozbilgin et al 2016) (Bengler et al 2014). In order to exploit the advantages of the combination of communication technology and autonomous driving that use artificial intelligence techniques, we designed a centralized decisionmaking strategy for autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles

abidine

Aknine

Bacha

2019

AAAI

View full text Add to dashboard Cite

This work explores the design of a central collaborative driving strategy between connected cars with the objective of improving road safety in case of highway on-ramp merging scenario. Based on a suitable method for predicting vehicle motion and behavior for a central collaborative strategy, a dynamic Bayesian network method that predicts the intention of drivers in highway on-ramp is proposed. The method was validated using real data of detailed vehicle trajectories on a segment of interstate 80 in Emeryville, California.

show abstract

“…The existing capacity analysis needs to be adapted to this many-to-many setting. The authors of [19] study the communication loads on a single vehicle, but obstructions and networking are not considered.…”

Section: Introductionmentioning

confidence: 99%

Performance and Scaling of Collaborative Sensing and Networking for Automated Driving Applications

Wang

Veciana

Shimizu

et al. 2018

2018 IEEE International Conference on Communications Workshops (ICC Workshops)

View full text Add to dashboard Cite

A critical requirement for automated driving systems is enabling situational awareness in dynamically changing environments. To that end vehicles will be equipped with diverse sensors, e.g., LIDAR, cameras, mmWave radar, etc. Unfortunately the sensing 'coverage' is limited by environmental obstructions, e.g., other vehicles, buildings, people, objects etc. A possible solution is to adopt collaborative sensing amongst vehicles possibly assisted by infrastructure. This paper introduces new models and performance analysis for vehicular collaborative sensing and networking. In particular, coverage gains are quantified, as are their dependence on the penetration of vehicles participating in collaborative sensing. We also evaluate the associated communication loads in terms of the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) capacity requirements and how these depend on penetration. We further explore how collaboration with sensing capable infrastructure improves sensing performance, as well as the benefits in utilizing spatiotemporal dynamics, e.g., collaborating with vehicles moving in the opposite direction. Collaborative sensing is shown to greatly improve sensing performance, e.g., improves coverage from 20% to 80% with a 20% penetration. In scenarios with limited penetration and high coverage requirements, infrastructure can be used to both help sense the environment and relay data. Once penetration is high enough, sensing vehicles provide good coverage and data traffic can be effectively 'offloaded' to V2V connectivity, making V2I resources available to support other in-car services.

show abstract

Evaluating the requirements of communicating vehicles in collaborative automated driving

Cited by 12 publications

References 18 publications

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles

Performance and Scaling of Collaborative Sensing and Networking for Automated Driving Applications

Contact Info

Product

Resources

About