Automated driving functions for traffic flow models to assess the traffic situation

Zusammenfassung

Nowadays, a detailed modelling of vehicle, sensors and environment is used for the development of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD). Due to the time consuming simulation effort which comes along with then high complexity of these models, only specific scenarios can be simulated. For the investigation of the traffic situation at motorway sections with a length of some hundred meters up to some kilometres a big amount of vehicles have to be simulated at the same time. Therefore, microscopic simulation tools like VISSIM or SUMO can be used. But traffic simulation models are not designed for the simulation of automated driving behaviour. Due to these missing driving functionalities, traffic simulation tools cannot be used for studying the impact of the “artificial” driving on traffic flow and traffic performance.

In this article, a method is presented, that allows a view on the traffic situation in the near future including conventional, assisted and automated driven vehicles. Therefore, the microscopic traffic simulation environment VISSIM is enhanced by realistic automated driving functions, wherein the estimation of the needed vehicle model detailing is essential. On the one hand, it has to be ensured that a high number of vehicles can be simulated to enable the generation of traffic flow relevant parameters. On the other hand, the simplified vehicle models must not influence the basic driving behaviour, which has a significant effect on traffic flow characteristics.

The longitudinal and lateral controller for automated driving functionalities are developed in MATLAB and coupled with VISSIM via its external driver interface. In VISSIM the user can classify the vehicles as conventional, assisted or automated, but also sub-classifications in the automation levels are possible. This enables a flexible modelling and so a high variety of automation and penetration is possible. In addition, the basic longitudinal and lateral behaviour models (speed controller and lane change controller) are extended by cooperative behaviour models representing systems like Truck-Platooning, Cooperative Adaptive Cruise Control or Cooperative Lane Chang Assistant.

The microscopic traffic flow model which is coupled with advanced vehicle controllers is used to simulate the interaction between individual vehicles in mixed traffic situations. The output of this micro-simulation is used in the macroscopic transport model to determine traffic characteristics like travel speed, link capacity and others. Using this method allows to analyse the impact on capacity of automated driving in mixed traffic situations on motorways and other major roads. It is used to demonstrate how various automation levels and different vehicle categories (passenger and commercial vehicles) influences the link capacity of basic freeway segments as well as merge, diverge and weaving segments. Moreover, special events like roadwork zone or traffic jam effect the track availability with focus on travel speed, link capacity and traffic density.