Lane Change Decision Making for Automated Driving

Blenk, Tabea; Cramer, Stephanie

doi:10.1145/3434073.3444686

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Fernández-Llorca et al (2020) introduced a two-stream networks approach for predicting the lane changes of surrounding vehicles. Blenk and Cramer (2021) showed that adjacent vehicles in both the target and current lanes had significant impacts on a driver's lane-changing decision-making. Such impacts have also been investigated in many other studies.…”

Section: Introductionmentioning

confidence: 99%

Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure

Zhang,

Zou,

Xie

et al. 2023

Computer aided Civil Eng

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A quantitative understanding of dynamic lane‐changing interaction patterns is indispensable for improving the decision‐making of autonomous vehicles (AVs), especially in mixed traffic with human‐driven vehicles. This paper develops a novel framework combining the hidden Markov model (HMM) and graph structure to identify the difference in dynamic interaction patterns between mandatory lane changes (MLC) and discretionary lane changes (DLC). An HMM is developed to separate the interaction patterns considering heterogeneity in lane‐changing processes and reveal the temporal properties of these patterns. Conditional mutual information is used to quantify the interaction intensity, and the graph structure is used to characterize the relationship between vehicles. Finally, a case study is conducted to demonstrate the practical value of the proposed framework and validate its effectiveness in predicting lane‐changing trajectories. Based on the lane‐changing events extracted from a real‐world trajectory dataset, the proposed analytical framework is applied to model MLC and DLC under congested traffic with levels of service E and F. The results show that there could be multiple heterogeneous dynamic interaction patterns in a lane‐changing process. A comparison of MLC and DLC demonstrates that MLC involves more intense interactions and more frequent transitions of the interaction network structure, while the evolution rules of interaction patterns in DLC do not exhibit a clear trend. The findings in this study are useful for understanding the connectivity structure between vehicles in lane‐changing interactions and for designing safe and smooth driving decision‐making models for AVs and advanced driver‐assistance systems.

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Section: Introductionmentioning

confidence: 99%

Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure

Zhang,

Zou,

Xie

et al. 2023

Computer aided Civil Eng

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“…Recently, however, HRI researchers have begun to explore the externalization or visualization of robotic navigation intent (e.g. path plans) as well [7,8,9,10,11,12], to enable more understandable robot's navigation behaviors, so as to improve trust and acceptance.…”

Section: Introductionmentioning

confidence: 99%

Projecting Robot Navigation Paths: Hardware and Software for Projected AR

Zhi-gang¹,

Parrillo²,

Wilkinson³

et al. 2021

Preprint

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For mobile robots, mobile manipulators, and autonomous vehicles to safely navigate around populous places such as streets and warehouses, human observers must be able to understand their navigation intent. One way to enable such understanding is by visualizing this intent through projections onto the surrounding environment. But despite the demonstrated effectiveness of such projections, no open codebase with an integrated hardware setup exists. In this work, we detail the empirical evidence for the effectiveness of such directional projections, and share a robot-agnostic implementation of such projections, coded in C++ using the widely-used Robot Operating System (ROS) and rviz. Additionally, we demonstrate a hardware configuration for deploying this software, using a Fetch robot, and briefly summarize a full-scale user study that motivates this configuration. The code, configuration files (roslaunch and rviz files), and documentation are freely available on GitHub at https://github.com/umhan35/arrow projection.

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