Comparing internal model control and sliding-mode approaches for vehicle yaw control
IEEE Transactions on Intelligent Transportation Systems
A solution to the ill-conditioned GPS positioning problem in an urban environment
IEEE Transactions on Intelligent Transportation Systems
Interoperable control architecture for cybercars and dual-mode cars
IEEE Transactions on Intelligent Transportation Systems
Virtual data mules for data collection in road-side sensor networks
MobiOpp '10 Proceedings of the Second International Workshop on Mobile Opportunistic Networking
Intelligent automatic overtaking system using vision for vehicle detection
Expert Systems with Applications: An International Journal
Cooperative controllers for highways based on human experience
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
The unmanned control of the steering wheel is, at present, one of the most important challenges facing researchers in autonomous vehicles within the field of intelligent transportation systems (ITSs). In this paper, we present a two-layer control architecture for automatically moving the steering wheel of a mass-produced vehicle. The first layer is designed to calculate the target position of the steering wheel at any time and is based on fuzzy logic. The second is a classic control layer that moves the steering bar by means of an actuator to achieve the position targeted by the first layer. Real-time kinematic differential global positioning system (RTK-DGPS) equipment is the main sensor input for positioning. It is accurate to about 1 cm and can finely locate the vehicle trajectory. The developed systems are installed on a Citroën Berlingo van, which is used as a testbed vehicle. Once this control architecture has been implemented, installed, and tuned, the resulting steering maneuvering is very similar to human driving, and the trajectory errors from the reference route are reduced to a minimum. The experimental results show that the combination of GPS and artificial-intelligence-based techniques behaves outstandingly. We can also draw other important conclusions regarding the design of a control system derived from human driving experience, providing an alternative mathematical formalism for computation, human reasoning, and integration of qualitative and quantitative information.