Cross-centerline crashes occur rarely in the United States but are especially severe. This type of crash is characterized by one vehicle departing over a centerline and encountering a vehicle traveling in the opposite direction. In recent years, automakers have started developing and implementing lane departure warning (LDW) on newer vehicles. This system provides the potential to reduce or significantly impact the frequency of cross-centerline crashes. The objective of this thesis was to estimate the potential crash and injury benefits of a LDW system if installed on every vehicle in the US fleet. This research includes the following 1) a characterization of cross-centerline crashes in the United States today with current and future prevention methods, 2) a reconstruction methodology used for all crashes including rollovers and heavy vehicles, and 3) a simulation model and approach method used to estimate potential benefits of LDW systems on cross-centerline crashes. Cross over to left crashes account for only 4% of non-junction non-interchange crashes but account for 44% of serious injury crashes of the same type. As part of this research, 42 cross-centerline crashes were reconstructed and simulated as if they had a LDW system installed. Accounting for driver capability to react to a LDW alert, crash reduction benefits ranged from 22 – 30%.Using injury risk curves, the probability of experiencing a MAIS2+ injury in a cross-centerline crash was reduced by 29% when using a LDW system. / Master of Science / Cross over to left crashes occur rarely but are typically very severe. Cross over to left crashes include wrong side of road crashes, cross over to left due to loss of control, and cross over to left over centerline crashes, also known as cross-centerline crashes. Cross-centerline crashes are typically very severe due to the high closing speeds of both vehicles. Lane departure warning (LDW) is a safety system developed by auto manufacturers designed to help drivers stay in their travel lane. Upon leaving your lane without using a turn signal, a LDW system will provide an alert to warn you to stay in your lane. While LDW systems have been found to be effective for preventing road departure crashes, there have been few studies on their effectiveness for preventing cross-centerline crashes. The research objective of this thesis was to estimate the number of crashes in the United States that would be avoided if every vehicle was equipped with a LDW system. It was also of interest to determine the number of front-row occupants who would not experience a greater than moderate level of injury (MAIS2+) with a LDW system installed. To form the dataset, 42 crashes were initially selected, reconstructed, and simulated as if the encroaching vehicle had a LDW system installed. The speed profile of the vehicle was constructed using crash simulation software and an approach model in order to predict the vehicle speed prior to the crash. Driver capability to react to a LDW warning was also accounted for resulting in a range of benefits. With a LDW system installed, 22- 30% of cross-centerline crashes would be avoided. The probability of experiencing a MAIS2+ injury was also reduced by 29% when a LDW system was installed.

Nowadays safety, along with efficiency, is one of the two strongest shaping forces of the automotive world, with advanced active safety applications being the major concentration of effort. Their development depends heavily on the quality of sensor data, a detailed measure of which is often up to the automotive manufacturers to derive, since the original equipment manufacturers (OEMs) may not disclose it on trade secrecy grounds. A model would not only provide a measure of the real-world performance of the sensor, but would also enable a higher degree of simulation accuracy which is vital to active safety function development. This is largely due to the high cost and risk involved in testing, a significant part of which is possible to be done in simulation alone. This thesis is an effort to derive a sensor model on behalf of Volvo Trucks of the performance of one of the most crucial sensors in current active safety - a lane detection camera.The work is focused on investigating approaches for modelling and simulation implementation of the lane estimation process within the black-box camera using reverse-engineering of the sensor's principles of operation. The main areas of analysis to define the factors that affect performance are the optics, image sensor, software and computer vision algorithms, and system interface. Each of them is considered separately and then methods for modelling are proposed, motivated, and derived accordingly. Finally, the finished model is evaluated to provide a measure of work success and a basis for further development. / Säkerhet är idag, tillsammans med effektivitet, en av de två starkaste förändringskrafterna i bilvärlden. Störst fokus ligger på avancerade aktiva säkerhetsfunktioner. Deras utveckling beror till stor del på kvaliteten på sensordata. En detaljerad modell för sensordata är ofta upp till fordonstillverkarna att härleda, eftersom tillverkare av sensorn ofta inte vill lämna ut sådan information. En modell ger inte bara ett mått på den verkliga prestandan hos sensorn, men ger också möjlighet till en högre grad av simuleringsnoggrannhet vilket är avgörande för utveckling av aktiva säkerhetsfunktioner. Tester är kostsamma och medför risker och en noggrann modell gör att tester kan utföras i simulering vilket minskar kostnader och risker. I denna avhandling härleds en sensormodell på uppdrag av Volvo Lastvagnar. Sensorn i fråga är en av de viktigaste sensorerna i det nuvarande aktiva säkerhetssystemet, kameran för att följa en körfil på vägen. Arbetet är fokuserat på undersökning av metoder för modellering och simulering av processen för filföljning baserat på sensorns funktionsprinciper. De viktigaste områdena för analys för att definiera de faktorer som påverkar prestanda är optik, bildsensorn, programvara, datorseendealgoritmer och systemets gränssnitt. Var och en av dessa behandlas separat och sedan föreslås och motiveras metoder för modellering. Slutligen utvärderas den färdiga modellen för att ge ett mått på hur framgångsrikt arbetet varit samt för att lägga en grund för ytterligare utveckling. / <p>The thesis work was carried out at Volvo Group Trucks Technology in Göteborg, Sweden. Supervisor for GTT: Mansour Keshavarz.</p>

This thesis project focuses on the verification method of a safety function called PICthat stands for Post-Impact Control which controls the vehicle motion of passengercars after being exposed to external disturbances produced by a 1st impact, aiming atavoiding or mitigating secondary events.The main objective was to select a promising method, among several candidates, todevelop further for testing the function and the interaction with the driver. To do thisis was first necessary to map the real destabilized states of motion that are targeted bythe function. These states are referred as Post-Impact problem space and are acombination of variables that describes the host vehicles motion at the instant thedestabilizing force has ceased. Knowing which states are requested by the solutioncandidates, it is possible to grade the rig candidates based on the capability ofcovering the problem space. Then, simulating the proposed rig solutions withMatlab/Simulink models to investigate which candidate fulfils best the problem space.The result of the simulations and other criteria is that a moving base simulator(Simulator SIM4) is most fitted to research verification. The second mostadvantageous solution is the rig alternative called Built-in Actuators.

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.

In the United States, there were 36,560 traffic-related fatalities in 2018, of which 20% were pedestrians, bicyclists, and other vulnerable road users (VRUs) [1]. Vulnerable road users are non-vehicle occupants who, because they are not enclosed in a vehicle, are at higher risk of injury in traffic crashes. While overall traffic fatalities in the US have been decreasing, pedestrian and bicyclist fatalities have been trending upward. Vehicle-based active safety features could avoid or mitigate crashes with VRUs, but are highly dependent on the ability to detect a VRU with enough time or distance. This work presents methods to examine the characteristics of vehicle-pedestrian and vehicle-bicycle crashes and near-crashes using a variety of data sources, assess the potential effectiveness of Automatic Emergency Braking (AEB) in avoiding and mitigating VRU crashes through modeling and simulation, and estimate the future benefits of AEB for VRU safety in the United States. Additionally, active safety features are most effective when behavior of VRUs can be anticipated, however, the behavior of pedestrians and bicyclists is notoriously unpredictable. Therefore, an approach to examine and categorize pedestrian behavior in response to near-crashes and crashes events is presented. Overall, findings suggest that AEB has great potential to avoid and mitigate collisions with pedestrians and bicyclists, but it cannot avoid all crashes even when an idealized AEB system is assumed. Most pedestrians and bicyclists were found to be visible for at least one second prior to the crash, but obstructions, the unpredictability of VRUs, and adverse weather/lighting conditions still pose challenges in avoiding and mitigating crashes with VRUs. / Doctor of Philosophy / In the United States, there were 36,560 traffic-related fatalities in 2018, of which 20% were pedestrians, bicyclists, and other vulnerable road users (VRUs) [1]. Vulnerable road users are non-vehicle occupants who, because they are not enclosed in a vehicle, are at higher risk of injury in traffic crashes. While overall traffic fatalities in the US have been decreasing, pedestrian and bicyclist fatalities are trending upward. Vehicle-based countermeasures, such as Automatic Emergency Braking (AEB), could avoid or mitigate crashes with VRUs, but are highly dependent on the ability to detect a VRU with enough time or distance. My work presents methods to examine the characteristics of vehicle-pedestrian and vehicle-bicycle crashes and near-crashes using a variety of data sources, assess the potential effectiveness of AEB in avoiding and mitigating VRU crashes through modeling and simulation, and estimate the future benefits of AEB for VRU safety in the United States. Additionally, crash avoidance technologies are most effective when behavior of VRUs can be anticipated, however, the behavior of pedestrians and bicyclists is notoriously unpredictable. Therefore, I examined and categorized pedestrian behavior in response to near-crashes and crashes events. Overall, we found that AEB has great potential to avoid and mitigate collisions with pedestrians and bicyclists, but it cannot avoid all crashes even when assuming an idealized AEB system. Most pedestrians and bicyclists were found to be visible for at least one second prior to the crash, but obstructions, the unpredictability of VRUs, and adverse weather/lighting conditions still pose challenges in avoiding and mitigating crashes with VRUs.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Location-Aware Adaptive Vehicle Dynamics (LAAVD) System is being developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. In contrast to current active safety systems, this system is predictive, not reactive. The LAAVD System employs a predictor-corrector method in which the driver's input commands (throttle, brake, steering) and upcoming driving environment (terrain, traffic, weather) are predicted. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin (PM), to assess the need to intervene. The driver's throttle and brake control are modulated to affect desired changes to the PM in a manner that is minimally intrusive to the driver's control authority. This system depends heavily on an understanding of the interplay between the vehicle's longitudinal, lateral, and vertical forces, as well as their resulting moments. These vehicle dynamics impact the PM metric and ultimately the point at which the Intervention Strategy will modulate the throttle and brake controls. Real-time implementation requires the development of computationally efficient predictive models of the vehicle dynamics. In this work, a method for predicting future vehicle states, based on current states and upcoming terrain, is developed using perturbation theory. An analytical relationship between the change in the spindle forces and the resulting change in the PM is derived, and the inverse relationship, between change in PM and resulting changes in longitudinal forces, is modeled. This model is implemented in the predictor-corrector algorithm of the Intervention Strategy. Corrections to the predicted states are made at each time step using a detailed, full, non-linear vehicle model. This model is run in real-time and is intended to be replaced with a drive-by-wire vehicle. Finally, the impact of this work on the automotive industry is discussed and recommendations for future work are given. / Master of Science

Road departure crashes are among the deadliest crash modes in the U.S. each year. In response, automakers have been developing lane departure active safety systems to alert drivers to impending departures. These lane departure warning (LDW) and lane departure prevention (LDP) systems have great potential to reduce the frequency and mitigate the severity of serious lane and road departure crashes. The objective of this thesis was to characterize lane and road departures to better understand the effect of systems such as LDW and LDP on single vehicle road departure crashes. The research includes the following: 1) a characterization of lane departures through analysis of normal lane keeping behavior, 2) a characterization of road departure crashes through the development and validation of a real-world crash database of road departures (NCHRP 17-43 Lite), and 3) develop enhancements to the Virginia Tech LDW U.S. fleetwide benefits model. Normal lane keeping behavior was found to vary with road characteristics such as lane width and road curvature. Consideration of the dynamic driving behaviors observed in the naturalistic driving study (NDS) data is important to avoid LDW false alarms and driver annoyance. Departure characteristics computed in normal driving were much less severe than the departure parameters measured in real-world road departure crashes. The real-world crash data collected in NCHRP 17-43 Lite database was essential in developing enhancements to the existing Virginia Tech LDW fleetwide benefits model. Replacement of regression model predictions with measured crash data and improvement of the injury criteria resulted in an 11-16% effectiveness for road departure crashes, and an 11-15% reduction in seriously injured drivers. / Master of Science / Road departure crashes account for nearly one-third of the roughly 30,000 automobile traffic fatalities in the U.S. each year. Lane departure warning (LDW) and lane departure prevention (LDP) systems are two safety systems developed to reduce the large number of fatalities resulting from road departures. The safety systems warn drivers if the vehicle begins to drift out of the intended lane of travel, and automatically steer the vehicle back into the lane of travel if it continues to drift. While LDW and LDP systems have potential to lower the number of fatal lane and road departure crashes, the technology is not yet a standard feature in production vehicles. There has been a lower than expected acceptance rate, and real-world benefits of the systems have not been published. The research objective for this thesis was to characterize lane and road departures to investigate the effect of these safety systems on road departure crashes. The first section of this thesis analyzed large amounts of time series data recorded from people in normal driving scenarios to model lane keeping behavior in non-crash, drift out of lane departures. We found driving behavior varied with road characteristics such as lane width and road curvature. These dynamic driving behaviors may lead to LDW false alarms and contribute to driver annoyance with the systems. The second portion of this research involved the development and validation of a real-world road departure crash database. The database included key departure parameters such as angle, speed, and road curvature. These parameters were used in the third section of the thesis to enhance the Virginia Tech LDW U.S. fleetwide benefits model, which is a mathematical trajectory simulation model that determines whether or not these road departure crashes could have been prevented if every vehicle in the U.S. was equipped with LDW. We found an effectiveness of 11-16% prevention for road departure crashes, and an 11-15% reduction in serious driver injury.

The interest for autonomous driving assistance, and in the end, self-driving cars, has increased vastly over the last decade. Automotive safety continues to be a priority for manufacturers, politicians and people alike. Visual-based systems aiding the drivers have lately been boosted by advances in computer vision and machine learning. In this thesis, we evaluate the concept of an end-to-end machine learning solution for detecting and classifying road lane markings, and compare it to a more classical semantic segmentation solution. The analysis is based on the frame-by-frame scenario, and shows that our proposed end-to-end system has clear advantages when it comes detecting the existence of lanes and producing a consistent, lane-like output, especially in adverse conditions such as weak lane markings. Our proposed method allows the system to predict its own confidence, thereby allowing the system to suppress its own output when it is not deemed safe enough. The thesis finishes with proposed future work needed to achieve optimal performance and create a system ready for deployment in an active safety product.

Demands for including more functions such as haptic guiding in power steering systems in road vehicles have increased with requirements on new active safety and comfort systems. Active safety systems, which have been proven to have a positive effect on overall vehicle safety, refer to systems that give the driver assistance in more and less critical situations to avoid accidents. Active safety features are going to play an increasingly important roll in future safety strategies; therefore, it is essential that sub systems in road vehicles, such as power steering systems, are adjusted to meet new demands. The traditional Hydraulic Power Assisted Steering, HPAS, system, cannot meet these new demands, due to the control unit's pure hydro-mechanical solution. The Active Pinion concept presented in this thesis is a novel concept for controlling the steering wheel torque in future active safety and comfort applications. The concept, which can be seen as a modular add-on added to a traditional HPAS system, introduces an additional degree of freedom to the control unit. Different control modes used to meet the demands of new functionality applications are presented and tested in a hardware-in-the-loop test rig. This thesis also covers various aspects of hydraulic power assisted steering systems in road vehicles. Power steering is viewed as a dynamic system and is investigated with linear and non-linear modeling techniques. The valve design in terms of area gradient is essential for the function of the HPAS system; therefore, a method involving optimization has been developed to determine the valve characteristic. The method uses static measurements as a base for calculation and optimization; the results are used in both linear and the non-linear models. With the help of the linear model, relevant transfer functions and the underlying control structure of the power steering system have been derived and analyzed. The non-linear model has been used in concept validation of the Active Pinion. Apart from concept validation and controller design of the active pinion, the models have been roven effective to explain dynamic phenomena related to HPAS systems, such as the chattering phenomena and hydraulic lag. / The printed version and the electronic version differ in that the electronic version contains two built in video films (see page 78 and page 89).

Multi-trailer articulated heavy vehicles (MTAHVs) exhibit unstable motion modes at high speeds, including jack-knifing, trailer swing, and roll-over. These unstable motion modes may lead to fatal accidents. On the other hand, these vehicle combinations have poor maneuverability at low speeds. Of all contradictory design criteria of MTAHVs, the trade-off relationship between the maneuverability at low speeds and the lateral stability at high speeds is the most important and fundamental. This trade-off relationship has not been adequately addressed. The goal of this research is to address this trade-off relationship through the design optimization of MTAHVs with active safety systems. A parallel design optimization (PDO) method is developed and applied to the design of MTAHVs with integrated active safety systems, which involve active trailer steering (ATS) control, anti-roll (AR) control, differential braking (BD) control, and a variety of combinations of these three control strategies. To derive model-based controllers, a single-trailer articulated heavy vehicle (STAHV) model with 5 degrees of freedom (DOF) and a MTAHV model with 7 DOF are generated. The vehicle models are validated with those derived using a commercial software package, TruckSim, in order to examine their applicability for the design optimization of MTAHVs with active safety systems. The PDO method is implemented to perform the concurrent design of the plant (vehicle model) and controllers. To simulate the closed-loop testing maneuvers, a driver model is developed and it is used to drive the virtual vehicle following the prescribed path. Case studies indicate that the PDO method is effective for identifying desired design variables and predicting performance envelopes in the early design stages of MTAHVs with active safety systems. / UOIT