Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening

Guo, Lie; Ge, Pingshu; Sun, Dachuan; Qiao, Yanfu

doi:10.3390/app10051635

Cited by 25 publications

(7 citation statements)

References 27 publications

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“…e number of vehicle types present and the relationship between the lateral and longitudinal characteristics and vehicle speed play a significant role in managing heterogeneous traffic behaviour in a mixed AV and HV. e current literature confirms that there are typical constraints in the car-following model [78] which are its rigidity to longitudinal vehicle dynamics parameters: safe distance, maximum speed, and acceleration/ deceleration rate. Besides, the recognition and integration of these traffic parameters that control the complex 2-dimensional vehicle behavioural models of traffic participants are critical tasks towards a new research direction.…”

Section: Research Gapmentioning

confidence: 70%

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Ozioko

Kunkel

Stahl

2022

Journal of Advanced Transportation

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Autonomous vehicles (AVs) are emerging with enormous potentials to solve many challenging road traffic problems. The AV emergence leads to a paradigm shift in the road traffic system, making the penetration of autonomous vehicles fast and its coexistence with human-driven cars inevitable. The migration from the traditional driving to the intelligent driving system with AV’s gradual deployment needs supporting technology to address mixed traffic systems problems, mixed driving behaviour in a car-following model, variation in-vehicle type control means, the impact of a proportion of AV in traffic mixed traffic, and many more. The migration to fully AV will solve many traffic problems: desire to reclaim travel and commuting time, driving comfort, and accident reduction. Motivated by the above facts, this paper presents an extensive review of road intersection mixed traffic management techniques with a classification matrix of different traffic management strategies and technologies that could effectively describe a mix of human and autonomous vehicles. It explores the existing traffic control strategies and analyses their compatibility in a mixed traffic environment. Then review their drawback and build on it for the proposed robust mix of traffic management schemes. Though many traffic control strategies have been in existence, the analysis presented in this paper gives new insights to the readers on the applications of the cell reservation strategy in a mixed traffic environment. Though many traffic control strategies have been in existence, the Gipp’s car-following model has shown to be very effective for optimal traffic flow performance.

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Section: Research Gapmentioning

confidence: 70%

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Ozioko

Kunkel

Stahl

2022

Journal of Advanced Transportation

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“…Nie and Farzaneh [ 189 ] focus on eco-driving ACC with an MPC algorithm for reduced fuel consumption and emissions while ensuring safety and comfort. Guo, Ge, Sun, and Qiao [ 190 ] introduce an MPC-based ACC with relaxed constraints to enhance fuel efficiency while considering speed limits and safety distances for driving comfort.…”

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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“…These proposed CF models are designed in such a way as to achieve certain objectives. Depending on various objectives, these models generate the velocity or acceleration profile of a vehicle to optimize specific policy targets including efficiency, safety, string stability, energy consumption, comfort and more [52,[66][67][68][69][70][71][72][73][74][75]. For instance, in a recent study, [66] developed a novel ACC algorithm based on model predictive control (MPC) and active disturbance rejection control (ADRC).…”

Section: Wiedemann Modelmentioning

confidence: 99%

“…In this study, the proposed model generates the acceleration profile of the following vehicle using the finite horizon constrained optimal control problem. In addition, [69] developed an ACC algorithm based on MPC and constraints softening. The aims is to optimize the CF requirements, safety, comfort, and economy.…”

Section: Mathematical Car Following Modelsmentioning

confidence: 99%

Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

Sadid

Antoniou

2023

IET Intelligent Trans Sys

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Microscopic traffic models (MTMs) are widely used for assessing the impacts of (connected) autonomous vehicles ((C)AVs). These models utilize car-following (CF) and lane-changing models to replicate the (C)AVs driving behaviors. Numerous studies are being lately published regarding the approximation of the driving behaviors of (C)AVs (especially CF behavior) with many state-of-the-art modelling methods. Still, there is no established CF model to mimic the accurate behavior of (C)AVs. Researchers often utilize existing mathematical CF models as well as limited data-driven models for (C)AVs modelling. Meanwhile, several studies conduct simulation-based impact assessments with various key performance indicators (KPIs). Identification of these KPIs is a crucial step for future studies. Hence, this paper presents a comprehensive outlook on different CF models with their adopted parameters for (C)AVs modelling and investigates how and in which aspects might the CF behaviors of (C)AVs are different from human-driven vehicles. In addition, the recent publications in data-driven CF models including their methodologies are explicitly discussed. This work also reviews simulation-based studies with the reported impacts and used KPIs. Finally, in light of the findings of this paper, several future research needs are highlighted.

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Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening

Cited by 25 publications

References 27 publications

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

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

Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state‐of‐the‐art

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