The total number of vehicles in the traffic network can vary without reducing the vehicle milage. The simulation results show that an increase in the use of self-driving vehicles does not automatically lead to less traffic. They focused on two forms of AV usage: car-sharing, where people use self-driving services privately, like today’s cars and ride-sharing, where AVs are shared with other passengers, who have a similar destination. In the virtual environment of PTV Visum, the researchers examined numerous possible developments. It is shown through the analysis of probability density functions characterising the CRI distribution that the reduction is not homogeneous across all indicator values, but depends on the penetration rate and the severity of the manoeuvre.The project brought together researchers and traffic analysts from Trivector and the Swedish knowledge centre for public transport, K2. As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. In this work, the CRI was first used to assess the risk during the merging manoeuvre. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy.
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