Vehicle Sensing and Dynamic Control
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".
Deadline for manuscript submissions: 15 July 2024 | Viewed by 1958
Special Issue Editors
Interests: vehicle dynamics; control of active safety systems; tire parameters estimation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
When a vehicle controller is developed, many aspects of the vehicle have to be taken into account, including the following: the vehicle model, the iteration between the tire and the road, the aerodynamics, the steering system, the braking system, the powertrain, and the suspension system. All of these aspects determine the development of novel control systems and algorithms, and help to avoid errors in the implementation of these systems in real vehicles.
Additionally, sensors play a pivotal role in providing sufficient information to control vehicle states. In addition, sensor filtering or observers provide the indirect measurements necessary for optimal control.
This Special Issue will address innovative research in the following areas:
The modeling and control of vehicle behavior: vehicle model, tire dynamic model, online learning and adaptation, model-based controller, linear-quadratic regulator (LQR), sliding mode control (SMC), H-infinity, and model predictive control (MPC).
Vehicle state estimation: Sensor fusion, Kalman Filtering (EKF, UKF), Recursive Least Squares (RLSs), Particle Filter, and Complementare Filter.
Active safety systems: development of intelligent control algorithms for anti-lock braking systems (ABSs), traction control systems (TCSs), electronic stability program (ESP), advanced driver assistance systems (ADASs), and the integration of related active safety features/devices into new vehicles.
The topics of interest include, but are not limited to, the following:
- Vehicle Dynamics Control
- Active Safety Systems
- Autonomous Driving Systems
- Identification and Estimation
- Steering, Braking, Tires, Suspension
- Advanced Driver Assistance Systems
- Driver–Vehicle Systems
- Electric Vehicles Model
Prof. Dr. Juan A. Cabrera
Dr. Javier Perez Fernandez
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Integrating LiDAR sensor data to Microsimulation Model Calibration for Proactive Safety Analysis
Authors: Igene, Morris; Liu, Hongchao; Bataineh, Tamer; Jimee, Keshav; Soltanirad, Mohammad; Bolden, Austin
Affiliation: Texas Tech University
Abstract: LiDAR sensors are becoming very popular for detecting trajectories of various road users, including vehicles, cyclists, and pedestrians with enhanced accuracy. Due to the high resolution and full detection penetration of LiDAR sensors, they provide real time spatiotemporal information, making them ideal for microscopic traffic analysis. Surrogate Safety Assessment Model (SSAM) is an automated tool that has been used with the microscopic traffic simulation models to identify conflicts. This approach faces a significant challenge because it is heavily dependent on the calibration accuracy of the microsimulation models. This paper introduces a two-step approach to calibrating microsimulation models using trajectory dataset from roadside LiDAR infrastructure. Four car-following and lane changing parameters of the Wiedmann 99 model were calibrated in PTV VISSIM using Directed Brute Force exhaustive search algorithm combined with Limited-Memory Broyden–Fletcher–Goldfarb–Shanno with Bounds (L-BFGS-B) optimization in the two-step approach for enhanced model accuracy. Vehicle conflicts identified from the calibrated models were employed in a Bivariate Extreme Value Theory near-crash analysis based on three surrogate safety indicators: PET, TTC and MaxD. The estimated average crash frequency (EACFs) obtained from the calibrated models were compared with historical crash frequency data and LiDAR sensor trajectory data.
