Variable Time Headway Autonomous Emergency Braking Control Algorithm Based on Model Predictive Control

Guo, Lie; Ge, Pingshu; Sun, Dachuan

doi:10.1109/cac51589.2020.9327238

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Gao et al [ 173 ] present a hardware-in-the-loop simulation platform for AEB system testing across various scenarios, ensuring reliability and effectiveness. Guo et al [ 174 ] introduce a variable time headway AEB algorithm using predictive modeling, combining radar and cameras. It adapts time headway for braking by considering speed, distance, and vehicle dynamics.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

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Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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“…An AEB model for the following vehicle is suggested that may dynamically adjust the critical braking distance under varied driving circumstances to avoid rear-end collisions, according to the recognized driver's intention provided through the Internet of Vehicles. In [14], based on model predictive control (MPC) theory, a variable time headway autonomous emergency braking (AEB) control method is suggested. This kind of interference is addressed by the variable time headway-based safety distance model.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous Vehicle with Emergency Braking Algorithm Based on Multi-Sensor Fusion and Super Twisting Speed Controller

2022

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The automobile revolution and growth in the number of cars produced several issues, and vehicle accidents remain one of the most serious road-related issues. Human mistakes and a failure to brake quickly are the main causes of accidents. There may be serious outcomes to driving when distracted. To address the aforementioned issues, an autonomous emergency braking system (AEBS) was developed. To support such an AEBS, scalable, reliable, secure, fault-tolerant, and interoperable technologies are required. An advanced emergency braking system (EBS) with sensor fusion is proposed in this paper that can autonomously identify a probable forward collision and activate the vehicle braking system to brake the vehicle to avoid or mitigate a collision. Additionally, it provides a non-linear speed controller that facilitates the AEBS to apply the brakes in an emergency. Sensor fusion using lidar, radar, and vision sensors makes the AEBS more efficient and more reliable to detect vehicles or obstacles and decreases the chance of collision to a minimum level. A MATLAB/Simulink environment was used for simulation experiments and the results demonstrated the stable operation of the AEBS to avoid forward collisions in the event of an error in the measurement of any one sensor while any vehicle is detected. The presented work establishes that the EBS sensor fusion unit is a highly reliable solution for detecting the leading vehicle at the proper time and the AEBS controller can apply the brake in the situation of forwarding obstacle detection.

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Smooth longitudinal driving strategy with adjustable nonlinear reference model for autonomous vehicles

Mohtavipour

Ehsan

Abeshoori

et al. 2023

Int. J. Dynam. Control

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Variable Time Headway Autonomous Emergency Braking Control Algorithm Based on Model Predictive Control

Cited by 6 publications

References 13 publications

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Autonomous Vehicle with Emergency Braking Algorithm Based on Multi-Sensor Fusion and Super Twisting Speed Controller

Smooth longitudinal driving strategy with adjustable nonlinear reference model for autonomous vehicles

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