ReviewSensors (Basel, Switzerland)2021
Collaborative Autonomous Driving-A Survey of Solution Approaches and Future Challenges.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
10 citing papers in PubMed.
- Performance Evaluation of Cooperative Driving Automation Services Enabled by Edge Roadside Units.Sensors (Basel, Switzerland) · 2026Article
- Information vs situation: balancing transparency and autonomy for trustworthy autonomous vehicles.Frontiers in robotics and AI · 2025Article
- Semantic Scene Completion in Autonomous Driving: A Two-Stream Multi-Vehicle Collaboration Approach.Sensors (Basel, Switzerland) · 2024Article
- Unlocking Mutual Gains-An Experimental Study on Collaborative Autonomous Driving in Urban Environment.Sensors (Basel, Switzerland) · 2023Article
- Collaborative Perception-The Missing Piece in Realizing Fully Autonomous Driving.Sensors (Basel, Switzerland) · 2023Article
- Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3GPP Standards.Sensors (Basel, Switzerland) · 2023Article
- DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots.Sensors (Basel, Switzerland) · 2023Article
- How Do Autonomous Vehicles Decide?Sensors (Basel, Switzerland) · 2022Review
- Simulation-Based Analysis of "What-If" Scenarios with Connected and Automated Vehicles Navigating Roundabouts.Sensors (Basel, Switzerland) · 2022Article
- Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.