The prediction results demonstrated that the method using six frames of variables as the input vectors for the BPNN model could improve the model prediction accuracy. Also, the number of nodes used in the hidden layer had a significant impact on the performance of the BPNN model. The results indicated that the best prediction accuracies in advance of a driver’s actual driving behavior with a lead time of 1s, 1.5s, and 1.8s were at 89.6%, 84.9%, 78.8% for merge events, and for non-merge events were at 92.2%, 87.5%, 81.1% respectively. / Recently, the applications of some driver assistance systems on vehicles have reduced vehicle accidents. However, studies have shown that the number of vehicle accidents caused by improper lane-changing behavior remains at a high level. Therefore, research has been focusing on developing a lane-changing assistance system to increase the safety level of driving in traffic. Many researchers have attempted to predict lane-changing behavior, and a general trend in the study of predicting driving behavior is the greater application of computational artificial intelligence. Artificial Neural Network (ANN) is one of the artificial intelligence methods, and it is well-known for its high reliability in a variety of applications. An ANN model can mimic human thinking and behavior due to its ability to capture the complex relationship among different variables in an environment of uncertainty. In this thesis, a BP (back-propagation) Neural Network model established by two methods was developed to predict a driver’s mandatory lane-changing decisions (merge or non-merge) at an early stage by considering driving environment features as the input vectors. Vehicle trajectory data from the Next Generation Simulation (NGSIM) dataset was used for training and testing the model. The results of the proposed model indicated that the prediction accuracies in advance of a driver’s actual driving behavior with a lead time of 1s, 1.5s, and 1.8s were at 89.6%, 84.9%, 78.8% for merge events, and for non-merge events were at 92.2%, 87.5%, 81.1% respectively. / Thesis / Master of Applied Science (MASc) / Lane-changing behavior at freeway on-ramps has a significant effect on driving safety and the stability of traffic flow. During the lane-changing process, the information processed by drivers is more complicated than that processed while remaining in a lane. If drivers fail to accurately judge the appropriate lane-changing time or the relative movement characteristics of related vehicles, vehicles accidents may occur. Thus, accurate prediction of lane-changing behavior is essential for a driving assistance system to ensure driver safety.

Microscopic simulation traffic models are widely used by transportation researchers and practitioners to evaluate and plan for transportation facilities. The intent of these models is to estimate the second-by-second vehicle movements and interactions on such facilities. Due to constraints related to time, budget, and availability of data, these models are typically designed in such a way where the microscopic output is viewed on the macroscopic level. Inherently, this can leave uncertainty to how the model estimates the individual interactions between vehicles on the microscopic level. This thesis utilizes three microsimulation models, INTEGRATION, VISSIM, and CORSIM, to investigate the lane changing behavior as vehicles approach a freeway diverge area. The count of lane changes, lane use distribution, and visual inspection of the simulated lane changing behavior was compared to video data collected at two freeway diverge areas on U.S. 460 in the vicinity of Blacksburg, Virginia during both off-peak and peak periods. It was observed that all three models generally overestimated the number of lane changes near the diverge areas compared to field observations. By modifying the models' lane changing logic, the models were able to closely match field observations in one of the four scenarios. It was found that microsimulation models accurately estimated the lane use distribution. In addition, the INTEGRATION lane use distribution results were found to be more consistent when compared to observed lane use distribution than either VISSIM or CORSIM. / Master of Science

Driving simulators and microscopic traffic simulation are important tools for making evaluations of driving and traffic. A driving simulator is de-signed to imitate real driving and is used to conduct experiments on driver behavior. Traffic simulation is commonly used to evaluate the quality of service of different infrastructure designs. This thesis considers a different application of traffic simulation, namely the simulation of surrounding vehicles in driving simulators. The surrounding traffic is one of several factors that influence a driver's mental load and ability to drive a vehicle. The representation of the surrounding vehicles in a driving simulator plays an important role in the striving to create an illusion of real driving. If the illusion of real driving is not good enough, there is an risk that drivers will behave differently than in real world driving, implying that the results and conclusions reached from simulations may not be transferable to real driving. This thesis has two main objectives. The first objective is to develop a model for generating and simulating autonomous surrounding vehicles in a driving simulator. The approach used by the model developed is to only simulate the closest area of the driving simulator vehicle. This area is divided into one inner region and two outer regions. Vehicles in the inner region are simulated according to a microscopic model which includes sub-models for driving behavior, while vehicles in the outer regions are updated according to a less time-consuming mesoscopic model. The second objective is to develop an algorithm for combining autonomous vehicles and controlled events. Driving simulators are often used to study situations that rarely occur in the real traffic system. In order to create the same situations for each subject, the behavior of the surrounding vehicles has traditionally been strictly controlled. This often leads to less realistic surrounding traffic. The algorithm developed makes it possible to use autonomous traffic between the predefined controlled situations, and thereby get both realistic traffc and controlled events. The model and the algorithm developed have been implemented and tested in the VTI driving simulator with promising results.

<p>Unsafe lane changes have been identified as a common factor in motor vehicle accidents. It would be helpful, particularly for automated vehicles, to know if there are behaviors of vehicles, beyond a directional signal, or characteristics of the traffic environment that correlated with a higher probability of an unsafe lane change (lane changes without a directional signal). This work investigates what the observable cues are that drivers use to determine the relative safety when overtaking front vehicles, and if drivers make more lane changes under certain conditions on highways. This study utilizes interviews, surveys, 3D animation software, and highway driving public footage for data collection and experiments. It is found that a side-to-side motion of the front vehicle or a factor that might trigger a side-to-side motion of the front vehicle in the environment is the key marker that indicates a possible unsafe lane change, and it is also found that traffic speed, time of day, traffic flow, and a combination of traffic density & number of lanes & vehicle count all have effects on drive’s decision on making lane changes on different levels.</p>

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Recent years have seen a renewed interest in Variable Speed Limit (VSL) strategies. New opportunities for VSL as a freeway metering mechanism or a homogenization scheme to reduce speed differences and lane changing maneuvers are being explored. This paper examines both the macroscopic and microscopic effects of different speed limits on a traffic stream, especially when adopting low speed limits. To that end, data from a VSL experiment carried out on a freeway in Spain are used. Data include vehicle counts, speeds and occupancy per lane, as well as lane changing rates for three days, each with a different fixed speed limit (80 km/h, 60 km/h, and 40 km/h). Results reveal some of the mechanisms through which VSL affects traffic performance, specifically the flow and speed distribution across lanes, as well as the ensuing lane changing maneuvers. It is confirmed that the lower the speed limit, the higher the occupancy to achieve a given flow. This result has been observed even for relatively high flows and low speed limits. For instance, a stable flow of 1942 veh/h/lane has been measured with the 40 km/h speed limit in force. The corresponding occupancy was 33%, doubling the typical occupancy for this flow in the absence of speed limits. This means that VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here, where speed limits have a reduced ability to limit flows. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Additionally, results show that lower speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rate. This means that VSL strategies aiming to homogenize traffic and reduce lane changing activity might not be successful when adopting such low speed limits. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane is avoided. These findings are useful for the development of better traffic models that are able to emulate these effects. Moreover, they are crucial for the implementation and assessment of VSL strategies and other traffic control algorithms.

In this thesis, multiple on-road vehicle tracking problem is explored, with greater consideration of road constraints and interactions between vehicles. A comprehensive method for tracking multiple on-road vehicles is proposed by making use of domain knowledge of on-road vehicle motion. Starting with raw measurements provided by sensors, bias correction methods for sensors commonly used in vehicle tracking are briefly introduced and a fast but effective bias correction method for airborne video sensor is proposed. In the proposed method, by assuming errors in sensor parameter measurements are close to zero, the bias is separately addressed in converted measurements of target position by a linear term of errors in sensor parameter measurements. Based on this model, the bias is efficiently estimated by addressing it while tracking or using measurements of targets that are observed by multiple airborne video sensors simultaneously. The proposed method is compared with other airborne video bias correction methods through simulations. The numerical results demonstrate the effectiveness of the proposed method for correcting bias as well as its high computational efficiency. Then, a novel tracking algorithm that utilizes domain knowledge of on-road vehicle motion, i.e., road-map information and interactions among vehicles, by integrating a car-following model into a road coordinate system, is proposed for tracking multiple vehicles on single-lane roads. This algorithm is extended for tracking multiple vehicles on multi-lane roads: The road coordinate system is extended to two-dimension to express lanes on roads and a lane-changing model is integrated for modeling lane-changing behavior of vehicles. Since the longitudinal and lateral motions are mutually dependent, the longitudinal and lateral states of vehicles are estimated sequentially in a recursive manner. Two estimation strategies are proposed: a) The unscented Kalman filter combined with the multiple hypothesis tracking framework to estimate longitudinal and lateral states of vehicles, respectively. b) A unified particle filter framework with a specifically designed computationally-efficient joint sampling method to estimate longitudinal and lateral states of vehicles jointly. Both of two estimation methods can handle unknown parameters in motion models. A posterior Cramer-Rao lower bound is derived for quantifying achievable estimation accuracy in both single-lane and multi-lane cases, respectively. Numerical results show that the proposed algorithms achieve better track accuracy and consistency than conventional multi-vehicle tracking algorithms, which assumes that vehicles move independently of one another. / Thesis / Doctor of Philosophy (PhD)

This thesis deals with the analysis of the car driving characteristics depending on the type of drive wheels. The first chapter defines the basic theoretical cars concept as well as procedures for determining the individual variables needed to identify the driving dynamics of vehicles. The practical part describes the plan and the progress of realized measurements on a selected pattern of vehicles, and the measured values are interpreted. The final part deals with the evaluation of the performed measurements and the data identified within these measurements.