Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions

The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag

Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model

Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure

An Agent-Based Exploration of the Hurricane Forecast-Evacuation System Dynamics

In the mainland US, the hurricane-forecast-evacuation system is uncertain, dynamic, and complex. As a result, it is difficult to know whether to issue warnings, implement evacuation management strategies, or how to make forecasts more useful for evacuations. This dissertation helps address these needs, by holistically exploring the system’s complex dynamics from a new perspective. Specifically, by developing – and using – an empirically informed, agent-based modeling framework called FLEE (Forecasting Laboratory for Exploring the Evacuation-system). The framework represents the key, interwoven elements to hurricane evacuations: the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between systems (forecasts and warning information, traffic). The dissertation’s first article describes FLEE’s conceptualization, implementation, and validation, and presents proof-of-concept experiments illustrating its behaviors when key parameters are modified. In the second article, sensitivity analyses are conducted on FLEE to assess how evacuations change with evacuation management strategies and policies (public transportation, contraflow, evacuation order timing), evolving population characteristics (population growth, urbanization), and real and synthetic forecast scenarios impacting the Florida peninsula (Irma, Dorian, rapid-onset version of Irma). The third article begins to explore how forecast elements (e.g., track and intensity) contribute to evacuation success, and whether improved forecast accuracy over time translates to improved evacuations outcomes. In doing so, we demonstrate how coupled natural-human models – including agent-based models –can be a societally-relevant alternative to traditional metrics of forecast accuracy. Lastly, the fourth article contains a brief literature review of inequities in transportation access and their implication on evacuation modeling. Together, the articles demonstrate how modeling frameworks like FLEE are powerful tools capable of studying the hurricane-forecast-evacuation system across many real and hypothetical forecast-population-infrastructure scenarios. The research compliments, and builds-upon empirical work, and supports researchers, practitioners, and policy-makers in hazard risk management, meteorology, and related disciplines, thereby offering the promise of direct applications to mitigate hurricane losses

Investigation of Factors Contributing to Fog-Related Single Vehicle Crashes

Fog-related crashes continue to be one of the most serious traffic safety problems in Florida. Based on the historical crash data, we found that single-vehicle crashes have the highest severity among all types of crashes under fog conditions. This study first analyzed the contributing factors of the fog-related single-vehicle crashes\u27 (i.e., off road/rollover/other) severity in Florida from 2011 to 2014 using association rules mining. The results show that lane departure distracted driving, wet road surface, and dark without road light are the main contributing factors to severe fog-related single vehicle crashes. Some suggested countermeasures were also provided to reduce the risk of fog-related single vehicle crashes. Since lane departure is one of the most important contributing factors to the single-vehicle crashes, an advanced warning system for lane departure under connected vehicle system was tested in driving simulation experiments. The system was designed based on the Vehicle-to-Infrastructure (V2I) with the concept of Augmented Reality (AR) using Head-Up Display (HUD). The results show that the warning with sound would reduce the lane departure and speed at curves, which would enhance the safety under fog conditions. In addition, the warning system was more effective for female drivers

Quantifying Drivers\u27 Responses to Failures of Semi-autonomous Vehicle Systems

The number of vehicles on the road with advanced and automated driving support systems (DSSs) is increasing. However, there may be some issues related to the implementation of DSSs in vehicles. One of those issues caused by the automated DSSs relates to the drivers\u27 being out-of-the-loop. As drivers\u27 roles are transitioned from system operators to systems supervisors (as in autonomous vehicles), drivers\u27 situation awareness of the driving surroundings may decrease which could negatively affect their responses when they need to take control of the vehicle from the malfunctioned (or failed) DSSs. Additionally, with both the adaptive cruise control (ACC) and lane keeping (LK) systems engaged, the longitudinal and lateral positions of the vehicle are under the control of automation and the vehicle becomes a semi-autonomous vehicle (i.e., the vehicles are now at level 2 automation based on the definitions of the National Highway Traffic Safety Administration taxonomy for automation). In semi-autonomous vehicles, drivers are more likely to interact with non-driving tasks and engage in risky behaviors (e.g., long glances away from the forward road way), as the demand of the driving tasks is much lower than manually driving and driving with only ACC engaged. This may worsen drivers\u27 responses to the failures of semi-autonomous vehicle components, when drivers are engaged in non-driving tasks. The objectives of this dissertation were to assess how drivers respond to the failures of the LK system with different levels of vehicle automation and to assess the effects of drivers\u27 engagements in non-driving tasks on their behaviors associated with a failure of the LK system. This dissertation also investigates if a lane departure warning would mitigate the negative effects of out-of-the-loop problem brought on by automation and improve drivers\u27 responses to the LK system fails especially when drivers are engaging both the ACC and LK systems. Additionally, the relationships between drivers\u27 personalities and attitudes toward automation and their responses during the failure of the LK system were evaluated. Three experiments were used to address the dissertation research objectives. The results demonstrate that drivers in semi-autonomous vehicles (level 2 automation vehicles) have less safe behaviors (e.g., more engagement in non-driving tasks and longer glances away from the roadway) than their peers who were manually driving the vehicles. During the failures of the LK systems, drivers in semi-autonomous vehicles have worse driving behaviors compared to their counterparts driving manually or driving with the LK system engaged. Non-driving tasks also increase drivers\u27 reaction time to safety critical events in semi-autonomous vehicles. However, the effects of audible lane departure warnings on drivers\u27 responses to potential lane departure events were not consistent between the level 0 automation condition (i.e., the manual driving condition) and level 2 automation condition (i.e., the automated driving condition). Overall, audible warnings with 1.48 s prediction time assist drivers\u27 in responding to the lane departure events following the failure of the LK system in semi-autonomous vehicles. However, the effects of audible warnings on drivers\u27 responses to the potential lane departure events are divergent when drivers are manually operating the vehicles. Though audible warnings as one type of discrete feedback of automation activities help drivers improve their responses to safety critical events in semi-autonomous vehicles, they cannot solve the out-of-control loop problem caused by automation. Future work should evaluate if continuous feedback could address the out-of-control loop problem brought on by automation and keep drivers in the vehicle control loop in semi- or fully- autonomous vehicles