Cloud-based traffic data fusion for situation evaluation of handover scenarios

TL;DR

This paper proposes a cloud-based data fusion approach to evaluate traffic situations for optimal vehicle handover timing in automated driving, enhancing safety and driver readiness.

Contribution

It introduces a novel cloud-based fusion system combining infrastructure, vehicle, and driver data to improve handover decision-making in urban scenarios.

Findings

Enhanced detection of critical traffic situations

Improved timing for driver handover decisions

Potential reduction in accident risks during automation transitions

Abstract

Upcoming vehicles introduce functions at the level of conditional automation where a driver no longer must supervise the system but must be able to take over the driving function when the system request it. This leads to the situation that the driver does not concentrate on the road but is reading mails for example. In this case, the driver is not able to take over the driving function immediately because she must first orient herself in the current traffic situation. In an urban scenario a situation that an automated vehicle is not able to steer further can arise quickly. To find suitable handover situations, data from traffic infrastructure systems, vehicles, and drivers is fused in a cloud-based situation to provide the hole traffic environment as base for the decision when the driving function should be transferred best and possibly even before a critical situation arises