Computational interaction models for automated vehicles and cyclists

Cyclists’ safety is crucial for a sustainable transport system. Cyclists are considered vulnerableroad users because they are not protected by a physical compartment around them. In recentyears, passenger car occupants’ share of fatalities has been decreasing, but that of cyclists hasactually increased. Most of the conflicts between cyclists and motorized vehicles occur atcrossings where they cross each other’s path. Automated vehicles (AVs) are being developedto increase traffic safety and reduce human errors in driving tasks, including when theyencounter cyclists at intersections. AVs use behavioral models to predict other road user’sbehaviors and then plan their path accordingly. Thus, there is a need to investigate how cyclistsinteract and communicate with motorized vehicles at conflicting scenarios like unsignalizedintersections. This understanding will be used to develop accurate computational models ofcyclists’ behavior when they interact with motorized vehicles in conflict scenarios.The overall goal of this thesis is to investigate how cyclists communicate and interact withmotorized vehicles in the specific conflict scenario of an unsignalized intersection. In the firstof two studies, naturalistic data was used to model the cyclists’ decision whether to yield to apassenger car at an unsignalized intersection. Interaction events were extracted from thetrajectory dataset, and cyclists’ behavioral cues were added from the sensory data. Bothcyclists’ kinematics and visual cues were found to be significant in predicting who crossed theintersection first. The second study used a cycling simulator to acquire in-depth knowledgeabout cyclists’ behavioral patterns as they interacted with an approaching vehicle at theunsignalized intersection. Two independent variables were manipulated across the trials:difference in time to arrival at the intersection (DTA) and visibility condition (field of viewdistance). Results from the mixed effect logistic model showed that only DTA affected thecyclist’s decision to cross before the vehicle. However, increasing the visibility at theintersection reduced the severity of the cyclists’ braking profiles. Both studies contributed tothe development of computational models of cyclist behavior that may be used to support safeautomated driving.Future work aims to find differences in cyclists’ interactions with different vehicle types, suchas passenger cars, taxis, and trucks. In addition, the interaction process may also be evaluatedfrom the driver’s perspective by using a driving simulator instead of a riding simulator. Thissetup would allow us to investigate how drivers respond to cyclists at the same intersection.The resulting data will contribute to the development of accurate predictive models for AVs

Harnessing Big Data for Characterizing Driving Volatility in Instantaneous Driving Decisions – Implications for Intelligent Transportation Systems

This dissertation focuses on combining connected vehicles data, naturalistic driving sensor and telematics data, and traditional transportation data to prospect opportunities for engineering smart and proactive transportation systems.The key idea behind the dissertation is to understand (and where possible reduce) “driving volatility” in instantaneous driving decisions and increase driving and locational stability. As a new measure of micro driving behaviors, the concept of “driving volatility” captures the extent of variations in driving, especially hard accelerations/braking, jerky maneuvers, and frequent switching between different driving regimes. The key motivation behind analyzing driving volatility is to help predict what drivers will do in the short term. Consequently, this dissertation develops a “volatility matrix” which takes a systems approach to operationalizing driving volatility at different levels, trip-based volatility, location-based volatility, event-based volatility, and driver-based volatility. At the trip-level, the dynamics of driving regimes extracted from Basic Safety Messages transmitted between connected vehicles are analyzed at a microscopic level, and where the interactions between microscopic driving decisions and ecosystem of mapped local traffic states in close proximity surrounding the host vehicle are characterized. Another new idea relates to extending driving volatility to specific network locations, termed as “location-based volatility”. A new methodology is proposed for combining emerging connected vehicles data with traditional transportation data (crash, traffic, road geometrics data, etc.) to identify roadway locations where traffic crashes are waiting to happen. The idea of event-based and driver-based volatility introduces the notion that volatility in longitudinal and lateral directions prior to involvement in safety critical events (crashes/near-crashes) can be a leading indicator of proactive safety.Overall, by studying driving volatility from different lenses, the dissertation contributes to the scientific analysis of real-world connected vehicles data, and to generate actionable knowledge relevant to the design of smart and intelligent transportation systems. The concept of driving volatility matrix provides a systems framework for characterizing the health of three fundamental elements of a transportation system: health of driver, environment, and the vehicle. The implications of the findings and potential applications to proactive network level screening, customized driver assist and control systems, driving performance monitoring are discussed in detail