  Accurate lane change prediction can reduce potential accidents and contribute
to higher road safety. Adaptive cruise control (ACC), lane departure avoidance
(LDA), and lane keeping assistance (LKA) are some conventional modules in
advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle
communication (V2V), vehicles can share traffic information with surrounding
vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies
on the vehicle's sensors to obtain the position and velocity of the leading
vehicle, CACC also has access to the acceleration of multiple vehicles through
V2V communication. This paper compares the type of information (position,
velocity, acceleration) and the number of surrounding vehicles for driver lane
change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD
dataset to predict lane change intention. Results indicate a significant
improvement in accuracy with an increase in the number of surrounding vehicles
and the information received from them. Specifically, the proposed model can
predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and
CACC scenarios, respectively.
