Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles

Du, Hao; Leng, Supeng; He, Junjia; Xiong, Kai; Zhou, Longyu

doi:10.1016/j.dcan.2023.06.002

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…,n min g (31) where ζ represents the variable to be optimized, h i (ζ) is the conditional constraint, and g(ζ) is the objective function. The optimization of the variable s=[s 1 ,s 2 ,⋯,s n ] T is computed as follows: (32) The outcome of solving the cost function, which adheres to all constraints, yields a continuous optimal velocity profile. The cost function is as follows:…”

Section: B Vehicle Speed Optimizationmentioning

confidence: 99%

Intelligent Vehicle Path Based on Discretized Sampling Points and Improved Cost Function: A Quadratic Programming Approach

Zhang,

2024

IEEE Access

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The paper introduces a quadratic programming algorithm for real-time local path planning of autonomous vehicles. The algorithm relies on discretized sampling points and an enhanced cost function. Initially, we formulate the cost function to optimize the reference trajectory and establish the Frenet coordinate system. The drivable region undergoes discretization to generate sampling points in the Frenet coordinate system. We apply the principles of convex spatial obstacle avoidance to define the vehicle's drivable area, taking into account the vehicle's kinematics and establishing barrier boundary conditions. Subsequently, quadratic programming is employed to determine an optimal path within the vehicle's drivable area. Concurrently, two cost functions are devised, the first evaluates the distance between the vehicle and obstacles, while the second assesses ride comfort, these cost functions are employed to evaluate sampling points and speed profiles, facilitating the planning of an optimal speed profile on the selected path. Finally, the algorithm undergoes validation through co-simulation using Matlab/Simulink, PreScan, and CarSim software. Various road scenarios, including straight and S-curve roads with both dynamic and static obstacles, are created to assess the method's feasibility. The test results demonstrate the algorithm's efficacy in avoiding moving and stationary obstacles and generating an ideal path compliant with driving conditions.INDEX TERMS Autonomous driving, path planning, quadratic programming, cost function

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Section: B Vehicle Speed Optimizationmentioning

confidence: 99%

Intelligent Vehicle Path Based on Discretized Sampling Points and Improved Cost Function: A Quadratic Programming Approach

Zhang,

2024

IEEE Access

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“…In order to solve the problem of obstacle avoidance in vehicle driving, Du et al design a vehicle obstacle avoidance framework based on a platoon. In this framework, they deploy DT in the head vehicle for trajectory planning to reduce communication overhead and decision delay [18]. On this basis, a trajectory planning algorithm is designed according to the urgency.…”

Section: Path Planningmentioning

confidence: 99%

“…By applying DT to the IoV, the driving conditions of all vehicles are acquired in real-time, and through interoperability with physical vehicles, the vehicles can upload information in real-time and receive real-time guidance from the twin to effectively avoid traffic risks. Additionally, the application of DT can provide optimal path planning for vehicles and predict traffic conditions by simulating the driving plans of different vehicles [18]. Such intelligent path planning can effectively avoid congestion, control traffic flow, and realize space management.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Enabled Internet of Vehicles Applications

Gao,

Peng,

Yoshinaga

et al. 2024

Electronics

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The digital twin (DT) paradigm represents a groundbreaking shift in the Internet of Vehicles (IoV) landscape, acting as an instantaneous digital replica of physical entities. This synthesis not only refines vehicular design but also substantially augments driver support systems and streamlines traffic governance. Diverging from the prevalent research which predominantly examines DT’s technical assimilation within IoV infrastructures, this review focuses on the specific deployments and goals of DT within the IoV sphere. Through an extensive review of scholarly works from the past 5 years, this paper provides a fresh and detailed perspective on the significance of DT in the realm of IoV. The applications are methodically categorized across four pivotal sectors: industrial manufacturing, driver assistance technology, intelligent transportation networks, and resource administration. This classification sheds light on DT’s diverse capabilities to confront and adapt to the intricate challenges in contemporary vehicular networks. The intent of this comprehensive overview is to catalyze innovation within IoV by providing an essential reference for researchers who aspire to swiftly grasp the complex dynamics of this evolving domain.

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Digital-Twin-Assisted Safety Control for Connected Automated Vehicles in Mixed-Autonomy Traffic

Wang,

Hao,

et al. 2025

IEEE Internet Things J.

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Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles

Cited by 5 publications

References 30 publications

Intelligent Vehicle Path Based on Discretized Sampling Points and Improved Cost Function: A Quadratic Programming Approach

Intelligent Vehicle Path Based on Discretized Sampling Points and Improved Cost Function: A Quadratic Programming Approach

Digital Twin-Enabled Internet of Vehicles Applications

Digital-Twin-Assisted Safety Control for Connected Automated Vehicles in Mixed-Autonomy Traffic

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