Longitudinal Dynamics during Lane Changes: Assessment of Automated Driving Styles under Real-World Conditions

Ossig, Johannes; Hinkofer, Simone; Cramer, Stephanie; Bengler, Klaus

doi:10.1109/itsc55140.2022.9922518

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The time for drivers to process the intention to change lanes significantly decreases when the turn signal is activated prior to the initiation of a lane change movement [9]. Further, activating the turn signal earlier is associated with significantly higher perceived cooperation compared to activating the turn signal later [8][9].…”

Section: Methodsmentioning

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: Methodsmentioning

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

View full text Add to dashboard Cite

show abstract

Time-Series-Based Personalized Lane-Changing Decision-Making Model

et al. 2022

Sensors

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In recent years, autonomous driving technology has been changing from “human adapting to vehicle” to “vehicle adapting to human”. To improve the adaptability of autonomous driving systems to human drivers, a time-series-based personalized lane change decision (LCD) model is proposed. Firstly, according to the characteristics of the subject vehicle (SV) with respect to speed, acceleration and headway, an unsupervised clustering algorithm, namely, a Gaussian mixture model (GMM), is used to identify its three different driving styles. Secondly, considering the interaction between the SV and the surrounding vehicles, the lane change (LC) gain value is produced by developing a gain function to characterize their interaction. On the basis of the recognition of the driving style, this gain value and LC feature parameters are employed as model inputs to develop a personalized LCD model on the basis of a long short-term memory (LSTM) recurrent neural network model (RNN). The proposed method is tested using the US Open Driving Dataset NGSIM. The results show that the accuracy, F1 score, and macro-average area under the curve (macro-AUC) value of the proposed method for LC behavior prediction are 0.965, 0.951 and 0.983, respectively, and the performance is significantly better than that of other mainstream models. At the same time, the method is able to capture the LCD behavior of different human drivers, enabling personalized driving.

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Longitudinal Dynamics during Lane Changes: Assessment of Automated Driving Styles under Real-World Conditions

Cited by 2 publications

References 17 publications

Coming In! Communicating Lane Change Intent in Autonomous Vehicles

Coming In! Communicating Lane Change Intent in Autonomous Vehicles

Time-Series-Based Personalized Lane-Changing Decision-Making Model

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