Simulation of cut-in by manually driven vehicles in platooning scenarios
DOI: 10.1109/itsc.2017.8317806
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This paper addresses the challenge of evaluating Cooperative Intelligent Transport Systems (C-ITS) in heterogeneous traffic environments, specifically focusing on scenarios where manually driven vehicles interact with automated platoons. While most existing simulations assume homogeneous fleets of connected, automated vehicles, real-world deployment will involve mixed traffic. The authors identify a gap in current research regarding the analysis of cut-in maneuvers performed by human-driven vehicles into platoons utilizing Cooperative Adaptive Cruise Control (CACC). To bridge this gap, the study presents a simulation framework that integrates a driving simulator with traffic and network simulators, allowing for the inclusion of human driver behavior in C-ITS scenarios. The experimental design utilizes a framework combining VTI’s driving simulation software, the microscopic traffic simulator SUMO, and the network simulator Veins, linked via the Plexe extension. This setup enables a human driver to control a vehicle within a simulated environment while interacting with three other vehicles operating under CACC. The study evaluates two distinct CACC controllers: the Rajamani controller, which uses a constant-distance gap strategy and relies on V2V communication for platoon leader data, and the Ploeg controller, which uses a constant-time gap strategy and relies primarily on radar data. The primary test case involves a manually driven vehicle, lacking V2V communication, performing a cut-in maneuver between the second and third vehicles of a platoon traveling at 90 km/h. The results demonstrate significant differences in how the two controllers handle the cut-in scenario. When the inter-vehicle gap was set to 17.5 meters, the Rajamani controller failed to prevent a collision during a cut-in with a large speed difference, as its reliance on platoon leader data caused it to accelerate into the merging vehicle. In contrast, the Ploeg controller successfully avoided collision by reacting to radar data, though it required emergency braking deceleration of 4.5 m/s². The study also found that the Rajamani controller remained vulnerable to collisions even with larger gaps if the platoon leader accelerated, whereas the Ploeg controller maintained stability. Both controllers exhibited string stability, meaning disturbances did not amplify through the platoon, but the Ploeg controller proved safer in handling non-communicating cut-ins due to its local sensor dependency. The significance of this work lies in its demonstration that CACC controller design must account for safety in heterogeneous traffic, not just string stability. The findings suggest that controllers relying heavily on V2V data from the platoon leader may be unsafe when interacting with non-communicating human drivers. The authors conclude that future CACC systems should incorporate hazard analysis frameworks and potentially switch to Adaptive Cruise Control (ACC) modes upon detecting cut-ins. Additionally, they highlight the need for improved sensor fusion, such as combining radar with cameras or LiDAR, to better detect and predict cut-in maneuvers by manually driven vehicles.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-20 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Methodological Resource: tool software