Driving field experiment data were used to investigate lane changes in which a slow lead vehicle was present to: 1) characterize lane changes, 2) develop predictive models, 3) provide collision avoidance system (CAS) design guidelines. A total of 3,227 slow lead vehicle lane changes over 23,949 miles were completed by sixteen commuters. Two instrumented vehicles, a sedan and an SUV, were outfitted with video, sensor, and radar data systems that collected data in an unobtrusive manner. Results indicate that 37.2% of lane changes are slow lead vehicle lane changes, with a mean completion time of 6.3 s; most slow lead vehicle lane changes are leftward, rated low in urgency and severity. A stratified sample of 120 lane changes was selected to include a range of maneuvers. On the interstate, lane changes are performed less often, <i>t</i>(30) = 2.83, <i>p</i> = 0.008, with lower urgency ratings, <i>F</i>(1, 31) = 5.24, <i>p</i> = 0.05, as compared to highway lane changes, as interstates are designed for smooth flow. Drivers who usually drive sedans are more likely to make lane changes than drivers of SUVs, <i>X</i> ²⁺(1)= 99.6247, <i>p</i> < 0.0001, suggesting that driving style is maintained regardless of which experimental vehicle is driven. Turn signals are used 64% of the time but some drivers signal after the lane change starts. Of cases in which signals are not used, 70% of them are made with other vehicles nearby. Eyeglance analysis revealed that the forward view, rearview mirror, and left mirror are the most likely glance locations. There are also distinct eyeglance patterns for lane changing and baseline driving. Recommendations are to use forward view or mirror-based visual displays to indicate presence detection, and auditory displays for imminent warnings. The "vehicle + signal" logistic regression model is best overall since it takes advantage of the distance to the front and rear adjacent vehicle, forward time-to-collision (TTC), and turn signal activation. The use of additional regressors would also improve the model. Five design guidelines are included to aid in the development of CAS that are useable, safe, and integrated with other systems, given testing and development. / Ph. D.

The goal of this work was to compensate for the effects of the Earth’s magnetic field in a vector field magnetic sensor. The magnetic sensor is a part of a low-cost crash avoidance system by Stephane Roussel where the magnetic sensor was used to detect cars passing when it was mounted to a test vehicle. However, the magnetic sensor’s output voltage varied when it changed orientation with respect to the Earth’s magnetic field. This limited the previous work to only analyze detection rates when the test vehicle travelled a single heading. Since one of the goals of this system is to be low-cost, the proposed solution for geomagnetic compensation will only use a single magnetic sensor and a consumer-grade GPS. Other solutions exist for geomagnetic compensation but use extra sensors and can become costly. In order to progress the development of this project into a commercial project, three separate geomagnetic compensation algorithms and a calibration procedure were developed. The calibration procedure compensated for the local magnetic field when the magnetic sensor was mounted to the test vehicle and allowed for consistent magnetic sensor voltage output regardless of the type of test vehicle. The first algorithm, Compensation Scheme 1 (CS1), characterized the local geomagnetic field with a mathematical function from field calibration data. The GPS heading was used as the input and the output is the voltage level of the Earth’s magnetic field. The second algorithm, Compensation Scheme 1.5, used a mathematical model of the Earth’s magnetic field using the International Geomagnetic Reference Field. An algorithm was developed to take GPS coordinates as an input and output the voltage contributed by the mathematical representation of the Earth’s magnetic field. The output voltages from CS1 and CS1.5 were subtracted from the calibrated magnetic sensor data. The third algorithm, Compensation Scheme 2 (CS2), used a high pass filter to compensate for changes of orientation of the magnetic sensor. All three algorithms were successful in compensating for the geomagnetic field and vehicle detection in multiple car headings was possible. Since the goal of the magnetic sensor is to detect vehicles, vehicle detection rates were used to evaluate the effectiveness of the algorithms. The individual algorithms had limitations when used to detect passing cars. Through testing, it was found that CS1 and CS1.5 algorithms were suitable to detect vehicles while stopped in traffic while the CS2 algorithm was suitable vehicle detection while the test vehicle is moving. In order to compensate for the limitations of the individual algorithms, a fused algorithm was developed that used a combination of CS1 and CS2 or CS1.5 and CS2. The vehicle speed was used in order to determine which algorithm to use in order to detect cars. Although the goal of this project is not vehicle detection, the rate of successful vehicle detection was used in order to evaluate the algorithms. The evaluation of the fused algorithm demonstrated the value of using CS1 and CS1.5 to detect vehicles when stopped in traffic, which CS2 algorithm cannot do. For a study conducted in traffic, using the fused algorithm increased vehicle detection rates by 51%-62% from using the CS2 algorithm alone. Since this work successfully compensated for geomagnetic effects of the magnetic sensor, the low-cost crash avoidance system can be further developed since it is no longer limited to driving in a single direction. Other projects that experience unwanted geomagnetic effects in their projects can also implement the knowledge and solutions used in this work.

Indiana University-Purdue University Indianapolis (IUPUI) / Automotive technology has grown from strength to strength in the recent years. The main focus of research in the near past and the immediate future are autonomous vehicles. Autonomous vehicles range from level 1 to level 5, depending on the percentage of machine intervention while driving. To make a smooth transition from human driving and machine intervention, the prediction of human driving behavior is critical. This thesis is a subset of driving behavior prediction. The objective of this thesis is to predict the possibility of crash and implement an appropriate active safety system to prevent the same. The prediction of crash requires data of transition between lanes, and speed ranges. This is achieved through a variation of hidden Markov model. With the crash prediction and analysis of the Markov models, the required ADAS system is activated. The above concept is divided into sections and an algorithm was developed. The algorithm is then scripted into MATLAB for simulation. The results of the simulation is recorded and analyzed to prove the idea.