Predicting Lane Changes by Identifying Sequence Patterns of Implicit Communication Cues

Hensch, Ann-Christin; Beggiato, Matthias; Krems, Josef F.

doi:10.1007/978-3-030-80012-3_1

Cited by 2 publications

(1 citation statement)

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“…In order to make AV lane change intent more human-like it is important to first establish how human drivers communicate and execute lane changes during real-world driving [1] [10]. An observation of real-world lane changes in Germany found that the most commonly used communication cues to indicate a lane change are movement toward the target lane and activation of the turn signal [4]. Other observed communication cues included: movement away from the target lane, steering angle/inclination angle, and acceleration.…”

Section: Introductionmentioning

confidence: 99%

Coming In! Communicating Lane Change Intent in Autonomous Vehicles

Lee

Britten

Block

et al. 2023

Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

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Lane changes of autonomous vehicles (AV) should not only succeed in making the maneuver but also provide a positive interaction experience for other drivers. As lane changes involve complex interactions, identification of a set of behaviors for autonomous vehicle lane change communication can be difficult to define. This study investigates different movements communicating AV lane change intent in order to identify which effectively communicates and positively affects other drivers' decisions. We utilized a virtual reality environment wherein 14 participants were each placed in the driver's seat of a car and experienced four different AV lane change signals. Our findings suggest that expressive lane change behaviors such as lateral movement have high levels of legibility at the cost of high perceived aggressiveness. We propose further investigation into how balancing key parameters of lateral movement can balance in legibility and aggressiveness that provide the best AV interaction experience for human drivers.

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Section: Introductionmentioning

confidence: 99%