Performance Guarantees on Machine-Learning-based Overtaking Strategies for Autonomous Vehicles

Németh, Balázs; Hegedüs, Tamás; Gáspár, Péter

doi:10.23919/ecc51009.2020.9143802

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The error in the simulation and experimental results is around 5%, which is mainly due to hardware non-linearities. Overtaking Technique Vision sensors, Steering wheel sensor [204] Machine Learning based overtaking strategy is presented with accurate collision avoidance ability. This paper provide a design method for desired trajectory.…”

Section: % Detection Rate Pedestrian Detectionmentioning

confidence: 99%

“…This methodology developed a generalized RL model which is capable of controlling a host vehicle from the previously unseen vehicle in an unseen trajectory without additional training. In [204], an ML-based trajectory design technique is presented for the overtaking process on the road. The paper also proposed a method of neural network trajectory design to determine the desired trajectory.…”

Section: % Detection Rate Pedestrian Detectionmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations. INDEX TERMSSmart Electric Vehicles (EVs), Driving Control Systems (DCS), Advanced Driving Assistance System (ADAS), Algorithms.

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Section: % Detection Rate Pedestrian Detectionmentioning

confidence: 99%

Section: % Detection Rate Pedestrian Detectionmentioning

confidence: 99%

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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A Lateral Control Based on Physics Informed Neural Networks for Autonomous Vehicles

Hegedűs,

Fényes,

Németh

et al. 2024

Lecture Notes in Mechanical Engineering

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In the paper, a lateral control strategy is presented using Physics-Informed Neural Network (PINN) for automated vehicles. The main idea is that the physics information is incorporated into the training process, which leads to an improvement in the performance level of the control algorithm. Moreover, in the highly nonlinear range of the lateral dynamics, which is not properly covered by the training dataset, the stability of the vehicle is guaranteed. The results are compared to a conventional neural network trained to control the vehicle.

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Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

2020

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In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator.

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Performance Guarantees on Machine-Learning-based Overtaking Strategies for Autonomous Vehicles

Cited by 3 publications

References 18 publications

A Review on Driving Control Issues for Smart Electric Vehicles

A Review on Driving Control Issues for Smart Electric Vehicles

A Lateral Control Based on Physics Informed Neural Networks for Autonomous Vehicles

Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

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