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Computer simulations of traffic and driving provide essential solutions to reduce risk and cost in traffic-related studies and research. Through nearly 90 years of simulation development, many research projects have attempted to improve the various aspects of realism through the use of traffic theory, cameras, eye-tracking devices, sensors, etc. However, the previous studies still present limitations, such as not being able to simulate mixed and chaotic traffic flows, as well as limited integration/interoperability with 3D driving simulators. Thus, instead of reusing previous traffic simulators, in this paper, we define relevant concepts and describe the development and testing of a novel traffic generator. First, we introduce realistic aspects to improve traffic generation, including interactive physics (i.e., interactions based on physics among the vehicles, infrastructure, and weather) and natural traffic behaviors (e.g., road user behaviors and traffic rules), allowing the self-driving vehicle behaviors to mimic human behaviors under stochastic factors such as random vehicles and speed. Second, we gain experiences from the technical deficiencies of existing systems. Third, we propose methods for traffic generation based on the action point angle of sight (APAS) formula, which adheres to these constraints and is interoperable with modern driving simulators. We also conducted quantitative evaluations in two experiments (comprising 250 trials), in order to prove that the proposed solution can effectively simulate mixed traffic flows. Moreover, the approaches presented in this study can help self-driving cars to find their way at an intersection/T-junction, as well as allowing them to steer automatically after an accident occurs. The results indicate that traffic generation algorithms based on these new traffic theories can be effectively implemented and used in modern driving simulators and multi-driving simulators, outperforming previous traffic generators based on repurposed technologies.