Divya Jayakumar Nair

Autonomous Vehicles: Disengagements, Accidents and Reaction Times

Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.

Fair allocation and cost-effective routing models for food rescue and redistribution

ABSTRACT The not-for-profit food rescue organizations play a vital role in alleviating hunger in many developing and developed countries. They rescue surplus food from the business sector and redistribute to welfare agencies supporting different forms of food relief. Routing and allocation decisions are critical in food rescue operations, in particular when there is a significant gap between supply and demand. However, there is a gap in the literature with regards to models that account for fairness in the allocation of limited rescued food along with efficient routing. We present three objective functions: utilitarian, egalitarian, and deviation-based for efficient and fair food allocation, and present a goal programming–based formulation combining cost-effective routing and allocation objectives to obtain balanced solutions. We propose and implement a heuristic solution algorithm for this food relief logistics problem and report numerical results from realistic food rescue instances.

Evaluating the Perception Index for Cyclists’ Safety Using Structural Equation Modelling

Transportation authorities across the globe are encouraging people to take up cycling for their daily activities. Even though individuals are aware about the advantages of cycling, there are a few barriers, which restrict them to use bicycles for their everyday commute. One of them, is the risk of getting hit from the rear by a motor vehicle travelling in the same direction. This study proposes a measure called perception index towards cyclist safety (PICS), to represent the level of insecurity among individual road users travelling on a corridor. A structural equation modeling framework is applied to express the perception index towards cyclist safety in terms of observed variables like road, road user, and environmental characteristics. This value is further used to predict an individual specific behavior and a preferred minimum overtaking distance. The contribution of this study is towards providing traffic planners, with a tool to forecast the reasonable minimum overtaking distance, based on the observed data that serves both safe cycling and smooth traffic conditions.