<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>

The development of Automated Driving Systems (ADS) has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Functional Safety (FuSa) and Operational Safety (OpSa). These terms of FuSa and OpSa are important, because ADS testing relies on both. Current recommendations for ADS testing include both OpSa and FuSa requirements. However, an examination of ADS safety requirements (e.g., industry reports, post-crash analysis reports, etc.) reveals that ADS safety arguments, in practice, depend almost completely on well-trained human operators, referred to in the industry as in vehicle fallback test drivers (IFTD). To date, the industry has never fielded a truly SAE L4 ADS on public roads due to this persistent hurdle of needing a human operator for Operational Safety. There is a tendency in ADS testing to reference International Standards Organization (ISOs) for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, ISOs for ADS end products are not necessarily applicable to ADS in development. With this in mind, there is a clear gap in the industry for unvalidated ADS literature. Because of this gap, ADS testing for unvalidated vehicles often relies on safety requirements for validated vehicles. This issue remains a significant challenge for ADS testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the ADS industry to develop a clear strategy for transitioning from an IFTD (Operational Safety) to an ADS (Functional Safety). Therefore, the purpose of this thesis is to present a framework for transitioning from Operational Safety to Functional Safety. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages Functional and Operational Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the ADS industry. / Master of Science / The development of Self-Driving Cars has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Robotic Control and Human Control. If a vehicle relies completely on Human Control, a human operator is responsible for all on-road safety. On the other hand, a fully autonomous would be considered fully in Robotic Control. These terms of Robotic Control and Human Control are important, because Self-Driving Car testing relies on both. Current recommendations for Self-Driving Car testing include both Robotic Control and Human Control requirements. However, an examination of Self-Driving Cars documentation (e.g., industry reports, post-crash analysis reports, etc.) reveals that Self-Driving Car safety arguments, in practice, depend almost completely on well-trained human operators. To date, the industry has never fielded a truly SAE L4 Self-Driving Car on public roads due to this persistent hurdle of needing a human operator for Human Control. There is a tendency in Self-Driving Car testing to reference standars for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, standards for Self-Driving Car end products are not necessarily applicable to Self-Driving Cars in development. With this in mind, there is a clear gap in the industry for unvalidated Self-Driving Car literature. Because of this gap, Self-Driving Car testing for unvalidated vehicles often relies on documentation for validated vehicles. This issue remains a significant challenge for Self-Driving Car testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the Self-Driving industry to develop a clear strategy for transitioning from Human Control to Robot Control. Therefore, the purpose of this thesis is to present a framework for transitioning from Human to Robot Control. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages all definitions of Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the Self-Driving industry.

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Autonomous Vehicles (AVs) should result in tremendous benefits to safe human transportation. Recent reports indicate a global average of 3,287 road crash related fatalities a day with the blame, in most cases, assigned to the human driver. By replacing the main cause, AVs are predicted to significantly reduce road accidents -- some claiming up to a 90% reduction on US roads. However, achieving these numbers is not simple. AVs are expected to assume tasks that human drivers perform both consciously and unconsciously -- in some instances, with Machine Learning. AVs incur new levels of complexity that, if handled incorrectly, can result in failures that cause loss of human life and damage to the environment. Accidents involving SAE Level 2 vehicles have highlighted such failures and demonstrated that AVs have a long way to go. The path towards safe AVs includes system architectures that provide effective failure monitoring, detection and mitigation. These architectures must produce AVs that degrade gracefully and remain sufficiently operational in the presence of failures. We introduce Safe-AV, a fault tolerant safety architecture for AVs that is based on the commonly adopted E-Gas 3 Level Monitoring Concept, the Simplex Architecture and guided by a thorough hazard analysis in the form of Systems-Theoretic Process Analysis (STPA). We commenced the architecture design with a review of some modern AV accidents which helped identify the types of failures AVs can present and acted as a first step to our STPA. The hazard analysis was applied to an initial AV architecture (without safety mechanisms) consisting of components that should be present in a typical AV (based on the literature and our ideas). Our STPA identified the system level accidents, hazards and corresponding loss scenarios that led to well-founded safety requirements which, in turn, evolved the initial architecture into Safe-AV. / Thesis / Master of Applied Science (MASc)