چكيده به لاتين
Today, adaptive cruise control systems play an important role in automotive technology. The
performance of adaptive cruise control systems has a significant impact on the level of
satisfaction of a self-driving car. Vehicles equipped with adaptive cruise control maintain
distance with the vehicle in front, control speed, and prevent accidents using their technology
and with the help of radar antennas, cameras, or laser rangefinders. However, adaptive cruise
controls are incapable in some cases such as detecting vehicles on curved roads. In this project,
3 solutions have been proposed to solve this problem. All three methods will be validated with
the help of data obtained from open-source maps and satellite images of roads, and finally, the
results will be compared and analyzed. With the help of road simulation, it is possible to
simulate various scenarios in which the performance of adaptive cruise control may be
impaired and there is a possibility of a collision between the ego vehicle and the target vehicle.
The performance of these methods is evaluated as appropriate so that the accuracy is close to
100% in suburban roads at distances of 300 meters or more, and it is up to 80% in the innercity roads up to distances of 200 meters, both of which are beyond the range of common
adaptive cruise control systems. Furthermore, the possibility of the collision caused by false
detection of target vehicle has been reduced to zero on all roads, and also no false detection of
non-target vehicles is reported in suburban roads or highways which could lead to unwanted
braking, reduced fuel efficiency, and passenger dissatisfaction.
