VNC 2017, IEEE Vehicular Networking Conference, November 27-29, 2017, Torino, Italy
Coordinated vehicle control strategies aim at optimizing driving dynamics to increase traffic flow without impacting safety. These control strategies are based on the knowledge of the vehicles' state information like position and velocity obtained through Vehicle-to-everything (V2X) communications. Literature on control strategies yet assumes perfect positions, whereas position errors are in fact present and non negligible (e.g. GPS). As a consequence, these localization errors impact the control strategies by introducing uncertainty, which must be accounted for to minimize the probability of accidents. This paper qualifies and quantifies such uncertainty and proposes strategies to reduce it in a collision avoidance scenario. We notably relate these strategies to their impacts on traffic flow. More specifically, we model coordinated automated vehicles as a Model Predictive Control (MPC), integrate localization errors and evaluate its impact of the output to avoid accident. We then propose possibilities to mitigate accident-prone controls and quantify them on traffic flow. Our study illustrates that localization errors impact traffic flow by forcing future automated vehicles to increase gaps or reduce speed.
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