On the public citation graph

Modeling Bev Potential Field Using Naturalistic Driving Behavior

https://doi.org/10.2139/ssrn.5125362
0 signed · 26 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-981-15-5281-6_9openalex-snapshot
Improved Potential Field Method for Robot Path Planning with Path Pruning
inferredcites →https://doi.org/10.1007/s12205-021-1585-5openalex-snapshot
A Dynamic Control Method for Cavs Platoon Based on the MPC Framework and Safety Potential Field Model
inferredcites →https://doi.org/10.1016/0378-4371(89)90108-8openalex-snapshot
The Yukawa potential
inferredcites →https://doi.org/10.1016/j.aap.2012.11.027openalex-snapshot
Utilizing support vector machine in real-time crash risk evaluation
inferredcites →https://doi.org/10.1016/j.aap.2020.105805openalex-snapshot
Risk perception and the warning strategy based on safety potential field theory
inferredcites →https://doi.org/10.1016/j.amar.2022.100252openalex-snapshot
A physics-informed road user safety field theory for traffic safety assessments applying artificial intelligence-based video analytics
inferredcites →https://doi.org/10.1016/j.eswa.2015.02.033openalex-snapshot
Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles
inferredcites →https://doi.org/10.1016/j.trc.2016.10.003openalex-snapshot
Driving safety field theory modeling and its application in pre-collision warning system
inferredcites →https://doi.org/10.1061/41186(421)420openalex-snapshot
A Unified Perspective on Traffic Flow Theory, Part I: The Field Theory
inferredcites →https://doi.org/10.1109/access.2021.3049393openalex-snapshot
A Macroscopic Model of Heterogeneous Traffic Flow Based on the Safety Potential Field Theory
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01339openalex-snapshot
Cross-view Transformers for real-time Map-view Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01680openalex-snapshot
SelfD: Self-Learning Large-Scale Driving Policies From the Web
inferredcites →https://doi.org/10.1109/cvpr52729.2023.02103openalex-snapshot
BEV-Guided Multi-Modality Fusion for Driving Perception
inferredcites →https://doi.org/10.1109/icra46639.2022.9812383openalex-snapshot
HDMapNet: An Online HD Map Construction and Evaluation Framework
inferredcites →https://doi.org/10.1109/itsc.2016.7795639openalex-snapshot
Automatic detection method research of incidents in China-FOT database
inferredcites →https://doi.org/10.1109/ivs.2019.8814248openalex-snapshot
Characteristics Analysis and Classification of Lane-changing Behavior after Following Process based on China-FOT
inferredcites →https://doi.org/10.1109/jiot.2016.2611605openalex-snapshot
LTE-V: A TD-LTE-Based V2X Solution for Future Vehicular Network
inferredcites →https://doi.org/10.1109/tie.2019.2898599openalex-snapshot
A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach
inferredcites →https://doi.org/10.1109/tits.2020.3008284openalex-snapshot
Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling
inferredcites →https://doi.org/10.1109/tiv.2023.3244193openalex-snapshot
Emergency Collision Avoidance and Mitigation Using Model Predictive Control and Artificial Potential Function
inferredcites →https://doi.org/10.1109/tiv.2023.3293954openalex-snapshot
BEV-V2X: Cooperative Birds-Eye-View Fusion and Grid Occupancy Prediction via V2X-Based Data Sharing
inferredcites →https://doi.org/10.1109/tiv.2024.3449278openalex-snapshot
BEVGPT: Generative Pre-trained Foundation Model for Autonomous Driving Prediction, Decision-Making, and Planning
inferredcites →https://doi.org/10.1109/tkde.2019.2912815openalex-snapshot
Reliable Accuracy Estimates from k-Fold Cross Validation
inferredcites →https://doi.org/10.1115/1.1386788openalex-snapshot
A Unified Approach to Driver Assistance Systems Based on Artificial Potential Fields
inferredcites →https://doi.org/10.1177/027836498600500106openalex-snapshot
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
inferredcites →https://doi.org/10.1177/0954407020985972openalex-snapshot
Research on risky driving behavior evaluation model based on CIDAS real data
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.