Abstract
While safety is the ultimate goal in designing Connected and Automated Vehicles (CAVs), current automotive safety standards fail to explicitly define rules and regulations that ensure the safety of CAVs or those interacting with such vehicles. This study investigates CAV safety in mixed traffic environments with both human-driven and automated vehicles, focusing particularly on rear-end collisions at intersections. The central hypothesis is that the primary reason behind these crashes is the potential mismatch between CAVs’ braking decisions and human drivers’ expectations. To test this hypothesis, various Artificial Intelligence (AI) techniques, along with specialized statistical methods are adopted to learn and model the braking behavior of human drivers at intersections and compare the results to that of CAVs. Findings suggest systematical differences in CAVs’ and humans’ braking trajectories, revealing a mismatch between their braking patterns. Accordingly, a Markovian decision modeling framework is adopted to design a novel CAV braking profile that ensures 1) compatibility with human expectation, and 2) safe and comfortable maneuvers by CAVs in mixed driving environments. The findings of this study are expected to facilitate the development of higher levels of vehicle automation by providing guidelines to prevent rear-end collisions caused by existing differences in CAVs’ and humans’ braking strategies.
Highlights
While safety is the ultimate goal in designing Connected and Automated Vehicles (CAVs), current automotive safety standards fail to explicitly define rules and regulations that ensure the safety of CAVs or those interacting with such vehicles
Focusing on CAVs’ most frequent crashes, the present study aims at approximating the braking behavior of human drivers at intersections and proposing a safe and human-like braking profile for CAVs
The focus of the present study is to investigate CAVs’ rear-end collisions at intersections in mixed traffic environments
Summary
While safety is the ultimate goal in designing Connected and Automated Vehicles (CAVs), current automotive safety standards fail to explicitly define rules and regulations that ensure the safety of CAVs or those interacting with such vehicles. The key factor in providing a reliable assessment of safety risks in mixed traffic environments with both traditional vehicles and CAVs is to identify the risk factors that contribute to crashes and near-crashes involving CAVs and human-driven vehicles Understanding these underlying factors is critical to ensure safety during the testing and deployment phases of the CAV technology. The primary motivation toward formulating this hypothesis is a recently published study by Waymo indicating that their CAVs are designed based on defensive driving standards; human drivers do not always follow/expect such behaviors (Waymo, LLC, 2017) Such instances can lead to crashes/near-crashes; for instance, from August 2016 to February 2017, 18 out of 26 crashes involving CAVs in California involved a CAV that was rear-ended by a human driver at an intersection (State of California Department of Motor Vehicles, 2017). The paper is concluded with a summary of the findings and future research needs
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