The result indicates that the distraction and subsequent elevated crash risk of texting while driving linger even after the texting event has ceased. This finding has safety and policy implications in reducing distracted driving.
The aim of this study is to develop a driving simulator test bed for a connected vehicle environment and study the impact of communicating safety messages on driver behavior.This was conducted by enabling a lead vehicle to communicate alert messages to the simulator when certain time-to-collision thresholds were reached.Thirty participants, grouped into aggressive and non-aggressive drivers, were allowed to drive the simulator twice; once with the alert messages, and another without the alert messages.Using time-to-collision as a performance measure, a t-test for dependent samples showed that for non-aggressive drivers, there were no differences in their driving behavior.However for aggressive drivers, their driving behavior showed a significant improvement in their overall safety.The findings not only lend credence to the safety benefits of the connected vehicles technology, but also means that a driving simulator test bed can be harnessed to achieve similar goals as physical test beds. Index Terms-connected vehicles, V2V, driving simulator, time-to-collision, test bed, aggressive drivers, and driving simulator test bed.
Eco-approach and departure is a complex control problem wherein a driver’s actions are guided over a period of time or distance so as to optimize fuel consumption. Reinforcement learning (RL) is a machine learning paradigm that mimics human learning behavior, in which an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the eco-driving problem, RL does not require prior knowledge of the environment to be learned and processed. This paper develops a deep reinforcement learning (DRL) agent for solving the eco-approach and departure problem in the vicinity of signalized intersections for minimization of fuel consumption. The DRL algorithm utilizes a deep neural network for the RL. Novel strategies such as varying actions, prioritized experience replay, target network, and double learning were implemented to overcome the expected instabilities during the training process. The results revealed the significance of the DRL algorithm in reducing fuel consumption. Interestingly, the DRL algorithm was able to successfully learn the environment and guide vehicles through the intersection without red light running violation. On average, the DRL provided fuel savings of about 13.02% with no red light running violations.
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