Abstract :
[en] In uncoordinated vehicular traffic, available road capacity cannot be fully exploited. This often leads to the formation of traffic jams and the emergence of vehicular traffic shock waves in times of temporarily high traffic demand. The main reason for congestion on highways is the combination of high traffic demand and small instabilities in the flow. These can be caused by bottleneck situations such as ramps, construction sites, accidents, or by small inaccuracies of human drivers. Traffic jams that are caused by the latter reason often are referred to as phantom jams. Even with modern vehicles, equipped with several sensors and driver supporting features, the drivers’ or sensors’ line of sight are already limited by the vehicle ahead. Hence, drivers or vehicles are not able to take anticipatory actions. To overcome these limitations it is necessary to investigate cooperative systems that are connected through a communication channel.
In this work, we introduce a novel distributed, connectionless and event-based communication protocol that enables us to eliminate up- stream shock wave formation already with low system penetration rates. Based on traffic information ahead, we propose a Cooperative Advanced Driver Assistance System (CADAS) that recommends pre-emptive velocity reductions in order to redistribute traffic more uniformly thereby eliminating traffic peaks. Simulation results show that our proposed CADAS increases and harmonizes the average velocity, and therewith reduces the overall travel time, avoiding unnecessary slowdowns. We also demonstrate that our event-based messaging scheme uses less network resources than beaconing.
Moreover, we conduct a field test on a private test track in order to validate our proposed protocol. We compare uncoordinated traffic to traffic, controlled by the proposed CADAS. With our experiments, we show that such a recommendation-based system can alleviate the formation of vehicular shock waves, thus improve vehicular traffic. Additionally, we perform simulations on the experiment scenario and compare the results to the empirical ones. With this comparison we show that our simulation results agree with the findings from the field test.
