Autonomous intelligent cruise control
DOI: 10.1109/25.260745
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the problem of traffic congestion and safety by proposing an Autonomous Intelligent Cruise Control (AICC) system for automatic vehicle following. The authors argue that human driving introduces reaction delays and errors that degrade traffic flow efficiency and safety. To mitigate these issues, they developed a non-cooperative automated control system that relies solely on onboard sensors to measure relative distance and velocity with the immediate preceding vehicle, without exchanging information with other vehicles. The primary goal was to design a control law that eliminates the "slinky" effect—where disturbances amplify down a platoon of vehicles—and suppresses oscillations, thereby achieving smoother and faster traffic flow than human drivers. The methodology involves developing a control law based on a constant time headway safety distance policy. This policy calculates a safe separation distance proportional to vehicle velocity, derived from a worst-case stopping scenario involving maximum deceleration and jerk constraints. The authors modeled vehicle dynamics using a first-order system with engine time constants and drag coefficients. They designed the longitudinal control law to satisfy specific stability and performance criteria, including ensuring that the frequency response magnitude remains less than one to prevent disturbance amplification. The system was evaluated through computer simulations comparing the AICC performance against three established human driver models: the linear follow-the-leader model, the linear optimal control model, and the look-ahead model. Simulations included steady-state traffic flow, transient responses, and emergency scenarios such as emergency stopping and cut-in maneuvers. The results demonstrate that the AICC system significantly outperforms the human driver models. The simulations showed that the AICC system completely eliminates slinky effects and considerably suppresses oscillations in inter-vehicle spacing, velocity, and acceleration. In contrast, the human models exhibited long settling times and significant oscillations. The AICC system achieved faster transient responses and allowed for shorter inter-vehicle safety spacings due to the elimination of human reaction delays. In emergency stopping simulations, where the lead vehicle decelerated at 0.8 g from 60 mph, all vehicles under AICC control stopped safely without collision. The system also successfully handled cut-in situations, identifying potential collision risks. The significance of this work lies in demonstrating that automated vehicle following can improve highway capacity and safety without requiring vehicle-to-vehicle communication. By using a constant time headway rule and appropriate control design, the system achieves stable, smooth traffic flow with higher throughput than manual driving. The findings suggest that partial automation, specifically adaptive cruise control, can effectively reduce congestion and enhance safety by removing human error and delay from the vehicle-following process. This provides a theoretical foundation for the development of intelligent transportation systems that utilize onboard sensors to optimize traffic dynamics.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Theoretical Contribution: computational model