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The problem of studying public transportation systems with autonomous vehicles is challenging because of behavioral differences that make existing models poorly fit and the technical difficulties involved in studying large autonomous systems operating on a grand scale. In this paper, we propose the following: (i) an autonomous transportation network setting; (ii) a method for modeling autonomous vehicles in simulation; and (iii) a high-performance simulation platform that allows analysis and visualization of transportation technologies. Results from microsimulation confirm theoretical benefits and improvements from employing autonomous systems in an example setting and highlight the platform's general ability to allow researchers to implement novel transportation systems and study the cost benefit variations occurring between them. Figure 4. Diagram of a bus instrumented to be autonomously driven with computing systems, a sensor suite, and networked communications equipment. 2272 S. LAM ET AL.

This article proposes a novel approach for the two-wheel-steer four-wheel-drive vehicle path tracking based on force control. Both the kinematic and dynamic models are used to calculate the virtual inputs based on a model predictive control algorithm. By using these virtual inputs as the constraints of a real-time sequential quadratic programming optimization, the optimal real inputs such as the drive forces and the steering angles are obtained. Note that the system is discretized when designing the control algorithm. The novelty is that all the forces are taken into account in the dynamic model, and the controller uses optimal steering angles and drive forces to obtain the minimum path offset. The integrated algorithms are then used in tracking a predefined path. Simulation results are provided to demonstrate the effectiveness and applicability of the proposed approach.