In order to prevent or mitigate the negative consequences of traffic crashes, automakers are developing active safety systems, which aim to prevent or mitigate collisions.  These systems are expensive to develop and as a result automakers and regulators are motivated to forecast the potential benefits of a proposed safety system before it is widely deployed in the vehicle fleet. The objective of this dissertation was to develop a methodology for predicting fleet-wide benefits for emerging crash avoidance systems as if all vehicles were equipped with a system.  Forward Collision Avoidance Systems (FCAS) were used as an example application of this methodology. The methodology developed for this research includes the following components: 1) identification of the target population, 2) development and validation of a driver model, 3) development of injury risk functions, 4) development of a crash severity reduction model, and 5) computation of fleet-wide benefits.  This dissertation presents a general methodology for each of these components that could be used for any active safety system.  Then a specific model is constructed for FCAS. FCAS could potentially be applicable to 31% of all collisions, 6% of serious injury crashes, and 7% of fatal crashes.  Annually, this accounts for 3.3 million collisions and 18,367 fatal crashes.  We developed a model of driver braking in response to a forward collision warning. Next we used logistic regression to develop injury risk functions that predicted the probability of injury given the crash severity ("V) and occupant characteristics.  Finally, we simulated 2,459 real-world rear-end collisions as if the driver had an FCAS with combinations of warnings, brake assist, and autonomous braking.  We found that between 3.4% and 7.2% of crashes could be prevented and that many more could be mitigated in severity.  These systems reduced the number of injured (MAIS2+) drivers in rear-end collisions between 32% and 55%.  In total, the systems could prevent between $184 and $338 million in economic costs associated with crashes per year. / Ph. D.

Lane change or "Side" Crash Avoidance Systems (SCAS) technologies are becoming available to help alleviate the lane change crash problem. They detect lane change crash hazards and warn the driver of the presence of such hazards. This thesis examines driver lane change behavior and evaluates the potential effectiveness of five warning onset rules for lane change or "side" crash avoidance system (SCAS) technologies. The ideal SCAS should warn the driver only when two conditions are met: (1) positive indication of lane change intent and (2) positive detection of a proximal vehicle in the adjacent lane of concern. Together, these two conditions create a crash hazard. The development of SCAS technologies depends largely on an understanding of driver behavior and performance during lane change maneuvers. By quantifying lane change behavior, real world crash hazard scenarios can be simulated. This provides an opportunity to evaluate potential warning onset rules or algorithms of driver intent to change lanes. Five warning onset rules for SCAS were evaluated: turn-signal onset (TSO), minimum separation (MS), line crossing (LC), time-to-line crossing (TLC), and tolerance limit (TL). The effectiveness of each rule was measured by the maximum response time available (tavailable) to avoid a crash for a particular lane change crash scenario, and by the crash outcome, crashed or crash avoided, of a particular lane change crash scenario. / Master of Science

<p>This master thesis deals with experimental data that were collected through a crash avoidance experiment (which was lead by Professor Lennart Strandberg) in February and March 1990. Fifty-two ordinary drivers were instructed to perform two different kinds of manoeuvres on ice to determine the effectiveness of antilock brakes and of four tyre configurations. Results were reported at the 1991 ESV Conference.</p><p>The first aim of this master thesis is to check and revise the measured data (used by Prof. Strandberg in courses at Linköping University). Checking out many hours of video recordings from onboard cameras reveals various protocol inconsistencies and errors, which in some cases it is not possible to correct. This work might increase the reliability of any further analysis of these data.</p><p>The second aim is to elaborate on the revised data and to test the hypothesis that quick steering is a key factor to not lose control of the car during a crash avoidance manoeuvre. Different variables are introduced and used to estimate the steering wheel velocity and lateral friction use.</p><p>The results show linear (positive) correlations between lateral friction use and steering wheel velocity. The greatest steering wheel velocities appear in the tests with loss-of-control and reach values up to 1180 degrees per second. However, the 1990 experimental layout was not intended for this type of research questions and it seems difficult to determine the causal relationship between quick steering and control of the car. Some cases of excessive steering input might have occurred. The results indicate that quick steering by itself is not enough to guarantee the total control of the car. An early reaction to the skid might be necessary too. Evidently, further research is needed.</p>

This master thesis deals with experimental data that were collected through a crash avoidance experiment (which was lead by Professor Lennart Strandberg) in February and March 1990. Fifty-two ordinary drivers were instructed to perform two different kinds of manoeuvres on ice to determine the effectiveness of antilock brakes and of four tyre configurations. Results were reported at the 1991 ESV Conference. The first aim of this master thesis is to check and revise the measured data (used by Prof. Strandberg in courses at Linköping University). Checking out many hours of video recordings from onboard cameras reveals various protocol inconsistencies and errors, which in some cases it is not possible to correct. This work might increase the reliability of any further analysis of these data. The second aim is to elaborate on the revised data and to test the hypothesis that quick steering is a key factor to not lose control of the car during a crash avoidance manoeuvre. Different variables are introduced and used to estimate the steering wheel velocity and lateral friction use. The results show linear (positive) correlations between lateral friction use and steering wheel velocity. The greatest steering wheel velocities appear in the tests with loss-of-control and reach values up to 1180 degrees per second. However, the 1990 experimental layout was not intended for this type of research questions and it seems difficult to determine the causal relationship between quick steering and control of the car. Some cases of excessive steering input might have occurred. The results indicate that quick steering by itself is not enough to guarantee the total control of the car. An early reaction to the skid might be necessary too. Evidently, further research is needed.

This dissertation uses quantitative analysis to provide insights for the urban and transportation policy-making process in order to manage two transportation externalities: road safety and air pollution in Bogotá Colombia. I performed a safety transportation risk analysis, which shows a high fatality and injury risk from road crashes in Bogota. I then analyzed safety-related benefits and costs of crash avoidance technology used in transit buses. My analysis reveals that despite of the life-safety benefit expected, Bogota’s values of statistical life and injuries make an investment on the technology for buses fall into the economically unjustified ranges. To analyze traffic related air pollution emissions, I developed a link-based emission model, which then it’s used to explore the traffic-related air pollution impacts of a highway capacity enhancement plan and a scrappage program for private cars. I use a bottom-up model that couples detailed activity data from a TAM, developed in EMME/4, with various emissions factors to develop a high-resolution road traffic emissions inventory for Bogotá. In particular, I use three emission models to produce the traffic related emission inventory, which includes exhaust emissions of five criteria air pollutants: Carbon monoxide (CO), Nitrogen Oxide (NOx), Sulphur oxides (SO2), Particulate Matter (PM, particles with diameters of 10 micrometers and smaller), and Volatile Organic Compound (VOC) emissions generated by hot-stabilized vehicle activity. The on-road vehicle emission model developed as part of this work marks an important turn over previous tools, because it opens the possibility to integrate environmental and transportation policy-making in Bogota. Integrating transportation and environmental policies has the potential to move the focus of environmental programs from “end-of-the-pipe” solutions to holistic analysis of how the land use, transportation systems and vehicle technology decisions play out on the levels of pollution in the city.

This report is a summary of the development of sensor integration for low-cost crash avoidance for over-land commercial trucks. The goal of the project was to build and test a system composed of low-cost commercially available sensors arranged on a truck trailer to monitor the environment around the truck. The system combines the data from each sensor to increase the reliability of the sensor using a probabilistic data fusion approach. A combination of ultrasonic and magnetoresistive sensors was used in this study. In addition, Radar and digital imaging were investigated as reference signals and possible candidates for additional sensor integration. However, the primary focus of this work is the integration of the ultrasonic and magnetoresistive sensors. During the investigation the individual sensors were evaluated for their use in the system. This included communication with vendors and lab and field testing. In addition, the sensors were modeled using an analytical mathematical model to help understand and predict the sensor behavior. Next, an algorithm was developed to fuse the data from the individual sensors. A probabilistic approach was used based on Bayesian filtering with a prediction-correction algorithm. Sensor fusion was implemented using joint a probability algorithm. The output of the system is a prediction of the likelihood of the presence of a vehicle in a given region near the host truck trailer. The algorithm was demonstrated on the fusion of an ultrasonic sensor and a magnetic sensor. Testing was conducted using both a light pickup truck and also with a class 8 truck. Various scenarios were evaluated to determine the system performance. These included vehicles passing the host truck from behind and the host truck passing vehicles. Also scenarios were included to test the system at distinguishing other vehicles from objects that are not vehicles such as sign posts, walls or railroads that could produce electronic signals similar to those of vehicles and confuse the system. The test results indicate that the system was successful at predicting the presence and absence of vehicles and also successful at eliminating false positives from objects that are not vehicles with overall accuracy ranging from 90 to 100% depending on the scenario. Some additional improvements in the performance are expected with future improvements in the algorithm discussed in the report. The report includes a discussion of the mapping of the algorithm output with the implementation of current and future safety and crash avoidance technologies based on the level of confidence of the algorithm output and the seriousness of the impending crash scenario. For example, irreversible countermeasures such as firing an airbag or engaging the brakes should only be initiated if the confidence of the signal is very high, while reversible countermeasures such as warnings to the driver or nearby vehicles can be initiated with a relatively lower confidence. The results indicate that the system shows good potential as a low cost alternative to competing systems which require multiple, high cost sensors. Truck fleet operators will likely adopt technology only if the costs are justified by reduced damage and insurance costs, therefore developing an effective crash avoidance system at a low cost is required for the technology to be adopted on a large scale.

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.

The aim of this research is to determine the effect of unsignalised intersection geometry on the rates of the various types of accidents occurring at unsignalised intersections. A literature review has identified that there is little consistency between the results of previous studies. Some studies found that particular parameters had an opposite effect to what was expected. With this in mind, the research identified reasons for these results and developed two basic approaches to mitigate some of the problems with multi-factor type studies. These approaches are 'maximise the efficiency of data collection' and 'develop techniques for analysing less than perfect data'. A database consisting of 206 unsignalised intersection sites from throughout Queensland was used for analysis. The outcome of this research confirms the validity of several of the current design standards for unsignalised intersections, in addition to identifying new engineering procedures.

<p>Ninety-five percent of all roadway crashes are attributed fully or partially to human error, and a multitude of safety-related programs, policies, and initiatives have seen limited success in reducing roadway crashes and their accompanying fatalities, injuries, and property damage. For this reason, safety professionals have lauded the emergence of autonomous vehicles (AVs) as a promising palliative to the persistent problem of road crashes. Such optimism is reflected in recent literature that have argues from a conceptual standpoint, that road safety enhancement will be one of the prospective benefits of AV operations because automation removes humans from vehicle driving operations and therefore criminates or mitigates human error. It can be argued that the safety benefits of AVs will be manifest when AV market penetration reaches 100%. However, it seems clear from a practical standpoint that the transition from a system of exclusively human-driven vehicles (HDVs) to that of exclusively AVs will not only be necessary but also an arduous journey. This transition period will be characterized by heterogeneous traffic, where human-driven vehicles (HDVs) and AVs share the road space, and whence the prospective safety benefits of AVs may not be fully realized due to human error arising from the HDV operations in the mixed traffic space. These traffic conflicts, which may lead to collisions, could arise from any of several contexts of driving maneuvers, one of which is aggressive lane changes, the focus of this thesis. From the literature, it is clear that lane changing is inherently more collision-prone compared to most other maneuvers including car following, and therefore the consequences of errant human driving behavior such as inattention of misjudgment during lane changing, are more severe. To address this problem, this thesis developed a control framework to be used by AVs to help them avoid collision in a mixed traffic stream with human drivers who exhibit aggressive lane-changing behavior. The developed framework, which is based on a Model Predictive Control (MPC) approach, is designed to control the AV’s movements safely by duly accommodating potential human error from the HDVs that could otherwise lead to any of two common collision patterns: rear-end and side-impact. Further, the thesis investigated how connectivity between the HDVs, and AVs could facilitate joint operational decision-making and sharing of real-time information, thereby further enhancing the safety of the entire traffic stream. Finally, the thesis presents the results of driving simulations carried out to test and validate the performance of the control framework under different traffic conditions.</p>