Date of Award
Doctor of Philosophy
Ruey Long Cheu
A lane changing event involves up to five vehicles: the subject vehicle, preceding and following vehicles in the original lane, and the preceding and following vehicles in the target lane. Understanding the behavior of the subject vehicle with respect to the surrounding vehicles is fundamental to the study of the safety of a lane change maneuver and for the modeling of lane changing behavior. First, the statistical properties of 10 lane changing parameters were defined and studied using the Next Generation SIMulation (NGSIM) vehicle trajectory data collected at the I-80 Freeway in Emeryville, California, and then tested with data collected at the U.S. Highway 101 in Los Angeles, California. The results show that all the parameters are positively correlated with each other; the gaps and distance are best described by the log-normal distribution; the time to collisions are best described by the Laplace probability distribution; the speed is best described by the logistic distribution. This dissertation then presents a Fuzzy Inference System (FIS) which models a drivers binary decision to or not to execute a discretionary lane changing move on freeways. It answers the following question Is it time to begin to move into the target lane? after the driver has decided to change lane and have selected the target lane. The system uses four input parameters: the gap between the subject vehicle and the preceding vehicle in the original lane, the gap between the subject vehicle and the preceding vehicle in the target lane, the gap between the subject vehicle and the following vehicle in the target lane, and the distance between the preceding and following vehicles in the target lanes. The input parameters were selected based on the outcomes of a drivers survey, and can be measured by sensors instrumented in the subject vehicle. The FIS was trained with NGSIM vehicle trajectory data collected at the I-80 Freeway in Emeryville, California, and then tested with data collected at the U.S. Highway 101 in Los Angeles, California. The test results show that the FIS system made lane change recommendations of yes, change lane with 82.2% accuracy, and no, do not change lane with 99.5% accuracy. These accuracies are better than the same performance measures given by the TRANSMODELERs gap acceptance model for discretionary lane change, which is also calibrated with NGSIM data. The developed FIS has a potential to be implemented in lane change advisory systems, in autonomous vehicles, as well as microscopic traffic simulation tools.
Received from ProQuest
Balal, Esmaeil, "A Discretionary Lane Changing Decision Model Based On Fuzzy Inference System" (2016). Open Access Theses & Dissertations. 603.