Evaluating performance of extended Kalman filter based adaptive cruise control using PID controller

Islam, Fahmida; Nabi, M M; Farhad, Mehedi; Peranich, Preston; Ball, John; Goodin, Chris

doi:10.1117/12.2585688

Cited by 11 publications

(8 citation statements)

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“…The vehicle control subsystem used the pure pursuit algorithm (Coulter, 1992) and a PID controller (Islam et al, 2021) to convert the proposed path into throttle and steering commands, respectively. In the first step of the pure pursuit algorithm, a local goal point is found on the path at a look‐ahead distance along the path from the current vehicle position.…”

Section: Agv Descriptionmentioning

confidence: 99%

Comparing real and simulated performance for an off‐road autonomous ground vehicle in obstacle avoidance

Carruth,

Goodin,

Dabbiru

et al. 2024

Journal of Field Robotics

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This field report presents the results of a study of obstacle detection and avoidance (ODOA) by an autonomous ground vehicle (AGV) in off‐road driving conditions. This study included both real and simulated testing of the AGV and served as the third and final phase of a 3‐year research project studying the influence of environmental conditions over autonomous driving. We compare and contrast the results of the real and experimental field testing and report our findings on the influence of soft soil in ODOA performance by an AGV. We find that rutting in soft soil results in higher throttle effort but lower steering effort and speed. The results of these experiments will inform future studies of autonomous driving on off‐road environments.

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Section: Agv Descriptionmentioning

confidence: 99%

Comparing real and simulated performance for an off‐road autonomous ground vehicle in obstacle avoidance

Carruth,

Goodin,

Dabbiru

et al. 2024

Journal of Field Robotics

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show abstract

“…12 These vehicles rely on a combination of sensors, artificial intelligence (AI), and machine learning (ML) algorithms to sense and interpret their surroundings, make decisions, and control their movements. 3 Safety is a paramount concern in autonomous driving, extending beyond the well-being of drivers and passengers to encompass pedestrians and other vehicles. Detection, facilitated by various sensors such as cameras, LiDAR, 4 and radar, 5 is crucial for the safe operation of Autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

Traffic light recognition and V2I communications of an autonomous vehicle with the traffic light for effective intersection navigation using YOLOv8 and MAVS simulation

Rahman,

Islam,

Ball

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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We integrate advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems for effective intersection navigation. In the first phase, the YOLOv8 deep learning model is employed to accurately detect traffic lights, with specialized training on the S2TLD Dataset for precision. Then we establish seamless V2I communication in MAVS Simulation, allowing vehicles to receive Signal Phase and Timing (SPaT) messages from traffic lights, enabling autonomous adjustment of speed and behavior. Simulating the scenarios in a high-fidelity automotive simulator demonstrates accurate traffic light detection and timely phase information, promising safer and more efficient intersection navigation for autonomous vehicles.

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“…The evolution of autonomous driving technology has been marked by the continuous integration of advanced features aimed at enhancing vehicle safety and performance. Some example features are adaptive cruise control (ACC), [1][2][3] lane centering control (LCC), [4][5][6] automatic intersection navigation (AIN), 7,8 etc. Lane centering is a critical component in autonomous navigation systems that assists vehicles in maintaining their position within designated lanes.…”

Section: Introductionmentioning

confidence: 99%

CNN-based end-to-end learning for lane centering

Ebu,

Islam,

Ball

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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Lane centering is a significant feature in the automotive industry and an important feature for advanced or autonomous vehicles, providing assistance to help drivers stay in their lane. The objective of this work is to utilize camera images which are processed by a lane-centering algorithm to make steering decisions. A model with a convolutional neural network (CNN) algorithm is used and simulated datasets are trained and tested. The goal is to make the program learn how to steer the vehicle autonomously and achieve end-to-end learning for steering command generation. The CNN can map raw pixels from cameras directly to steering commands without any intermediate feature engineering. A model is utilized which is created by NVIDIA researchers called the NVIDIA PilotNet. This network comprises five convolutional layers for feature extraction, followed by three fully connected layers for predicting the steering commands. The model is trained using two different sets of data to see how well the model performs with different types of data. The first set comes from Udacity's Self-Driving Car Nanodegree Program, which uses their open-source Vehicle Simulation.Secondly, a dataset from the Mississippi State University Autonomous Vehicular Simulator (MAVS) is used. The training process involves reducing the error between predicted steering angles and the actual steering commands logged by the car. After the model has been trained, it is implemented for testing the car's autonomous capabilities within the Self Driving Car Nanodegree Program Simulation. In this mode, the car demonstrates the ability to effectively track and navigate along the road lanes.

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Evaluating performance of extended Kalman filter based adaptive cruise control using PID controller

Cited by 11 publications

References 0 publications

Comparing real and simulated performance for an off‐road autonomous ground vehicle in obstacle avoidance

Comparing real and simulated performance for an off‐road autonomous ground vehicle in obstacle avoidance

Traffic light recognition and V2I communications of an autonomous vehicle with the traffic light for effective intersection navigation using YOLOv8 and MAVS simulation

CNN-based end-to-end learning for lane centering

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