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Controller Configurations

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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Published on: December 18, 2020

Personalized active collision avoidance trajectory planning and variable time domain control integrating driver

Xiaochuan Zhou¹, Mengyue Qu¹, Changzhi Zhou¹

¹Department of Vehicle Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China.

Accident; Analysis and Prevention

|September 10, 2024

Summary

This summary is machine-generated.

This study introduces personalized collision avoidance control for vehicles, adapting to individual driver behaviors. The novel system enhances safety and comfort by tailoring interventions to driver characteristics, improving trajectory tracking and reducing workload.

Keywords:

Active collision avoidance Driver characteristics Personalized control Trajectory planning

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Area of Science:

Vehicle active safety systems
Human-machine interaction in automotive engineering
Control systems engineering

Background:

Existing driver assistance systems often fail to account for individual driver characteristics, limiting optimal performance and personalized intervention.
Effective collision avoidance requires controllers that adapt to diverse driving behaviors and preferences.

Purpose of the Study:

To propose a novel vehicle active collision avoidance control strategy that considers individual driver characteristics.
To enhance the performance and comfort of driver assistance systems through personalized control.

Main Methods:

Collected and analyzed lane-changing and collision avoidance data from 10 drivers.
Developed a comprehensive index integrating trajectory tracking and driver burden to characterize drivers.
Designed a personalized time-variable domain Model Predictive Control (MPC) incorporating driver-specific trajectories.

Main Results:

A personalized collision avoidance characteristic curve was derived based on driver data.
A sixth-order polynomial trajectory planning method optimized for driver matching and vehicle stability was implemented.
Driver-in-the-loop tests confirmed the personalized MPC improved trajectory tracking and reduced driver workload.

Conclusions:

The proposed personalized MPC strategy effectively matches diverse driving characteristics for collision avoidance.
Tailoring control to individual drivers enhances safety, comfort, and overall driving experience.
This approach represents a significant advancement in adaptive driver assistance systems.