[Paper Review] Development of a Connected and Automated Vehicle Longitudinal Control Model

Mizanur Rahman, Rafiul Islam+2 more|arXiv (Cornell University)|Jan 1, 2020

Traffic control and management25 references4 citations

TL;DR

This paper proposes an Information-Aware Driver Model (IADM) for connected and automated vehicles (CAVs) that fuses real-time sensor data and V2X communications to enable safe, comfortable, and efficient longitudinal control. The IADM autonomously adjusts acceleration and deceleration using a speed- and reaction time-dependent safe gap function, achieving string stability and local stability without recalibration across traffic conditions, with performance tuned to passenger preferences.

ABSTRACT

It is envisioned that, in the future, most vehicles on our roadway will be controlled autonomously and will be connected via vehicle to everything (V2X) wireless communication networks. Developing a connected and automated vehicle (CAV) longitudinal controller, which will consider safety, comfort and operational efficiency simultaneously, is a challenge. A CAV longitudinal controller is a complex system where a vehicle senses immediate upstream vehicles using its sensors and receives information about its surroundings via wireless connectivity, and move forward accordingly. In this study, we develop an information-aware driver model (IADM) that utilizes information regarding an immediate upstream vehicle of a subject CAV through CAV sensors and V2X connectivity while considering passenger comfort and operational efficiency along with maintaining safety gap for longitudinal vehicle motion of the autonomous vehicle. Unlike existing driver models for longitudinal control, the IADM intelligently fuses data received from in vehicle sensors, and immediate upstream vehicles of the subject CAV through wireless connectivity, and IADM parameters do not need to be calibrated for different traffic states, such as congested and non congested traffic conditions. It only requires defining the subject CAVs maximum acceleration and deceleration limit, and computation time that is needed to update the subject CAVs trajectory from its previous state. Our analyses suggest that the IADM (i) is able to maintain safety using a newly defined safe gap function depending on the speed and reaction time of a CAV; (ii) shows local stability and string stability and (iii) provides riding comfort for a range of autonomous driving aggressiveness depending on the passenger preferences.

Motivation & Objective

• To develop a longitudinal controller for connected and automated vehicles (CAVs) that ensures safety, comfort, and operational efficiency simultaneously.
• To eliminate the need for recalibrating controller parameters across different traffic states such as congested and non-congested flow.
• To design a controller that intelligently fuses data from onboard sensors and V2X-connected upstream vehicles.
• To ensure string stability and local stability in platoon dynamics without external tuning.

Proposed method

• The IADM uses a safe gap function dependent on CAV speed and reaction time to maintain longitudinal safety.
• It integrates real-time data from in-vehicle sensors and V2X communications with upstream vehicles to inform trajectory decisions.
• The controller computes new trajectories based on maximum acceleration and deceleration limits and a fixed computation update time.
• The model avoids parameter recalibration by relying only on vehicle-specific limits and update frequency.
• String stability is ensured through a feedback mechanism that suppresses error propagation in vehicle platoons.
• Passenger comfort is dynamically adjusted by tuning the aggressiveness of the control response based on preference settings.

Experimental results

Research questions

• RQ1 Can a unified longitudinal controller be developed that maintains safety, comfort, and efficiency without recalibration across varying traffic states?
• RQ2 How can V2X and onboard sensor data be fused effectively to improve longitudinal control performance in CAVs?
• RQ3 Does the proposed IADM ensure string stability and local stability in vehicle platoons?
• RQ4 To what extent can passenger comfort be adjusted without compromising safety or efficiency?
• RQ5 Can the safe gap function be made adaptive to speed and reaction time while maintaining robustness?

Key findings

• The IADM successfully maintains safety through a speed- and reaction time-dependent safe gap function, ensuring consistent headway under varying conditions.
• The controller demonstrates local stability and string stability, preventing error amplification in vehicle platoons.
• The model achieves riding comfort across a range of autonomous driving aggressiveness levels, adaptable to passenger preferences.
• No parameter recalibration is required for different traffic states, reducing implementation complexity.
• The controller operates using only maximum acceleration/deceleration limits and computation update time, simplifying deployment.
• The system maintains performance across diverse traffic conditions without re-tuning, as validated through simulation and analysis.