Anonymous

Abstract

Drivers frequently encounter significant challenges when making left turns onto main roads or merging into traffic, primarily due to difficulties in accurately assessing gaps in oncoming traffic. A critical concern arises from the misperception of approaching vehicle speeds and distances, a problem greatly exacerbated by the increasing prevalence of Electric Vehicles (EVs) and other quick-accelerating vehicles.

EVs deliver maximum torque instantaneously, allowing many to accelerate from 0 to 60 mph in under 5.0 seconds. This unique capability results in different acceleration characteristics compared to Internal Combustion Engine (ICE) vehicles, with some studies noting that EVs may accelerate more quickly. As a result, drivers and vehicle systems may need to adjust their timing strategies to account for these differences. Traditional ICE drivers often rely on learned perceptions of vehicle acceleration and braking that do not accurately apply to EVs. This discrepancy creates a "perception gap," leading drivers to misjudge available time and distance for a safe maneuver, as an EV can approach much faster than anticipated, which can reduce the time available for turning opportunities. Such misjudgments can result in unexpected driving situations or require sudden maneuvers. For efficiency-driven operations like UPS, the amount of left turns on a route is minimized due to their inherent complexity and the additional time they may require.

The primary question addressed by this publication is how vehicle intelligence can be effectively leveraged to detect the type of incoming vehicle and provide timely, relevant information to the driver, supporting informed decision-making during crossing (left turns) or merging maneuvers.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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