Autonomous driving and traffic systems

Articles

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  Symmetry breaking in optimal transport networks

  Finding an optimal shape for transport networks, represented as multilayer structures, is a challenging problem. The authors propose analytical and computational frameworks to analyze sharp transitions from symmetric to asymmetric shapes in optimal networks, that can be applied for planning and development of improved multimodal transportation systems within a city.

  • Siddharth Patwardhan
  • Marc Barthelemy
  • Filippo Radicchi

  ArticleOpen Access4 May 2024 Nature Communications
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  Traffic light optimization with low penetration rate vehicle trajectory data

  Without relying on any infrastructure-based vehicle detectors, the authors present a scalable traffic signal re-timing system that uses a small percentage of connected vehicle trajectories as the only input. Real-world tests demonstrate that the system decreases both delays and number of stops.

  • Xingmin Wang
  • Zachary Jerome
  • Henry X. Liu

  ArticleOpen Access20 Feb 2024 Nature Communications
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  Online legal driving behavior monitoring for self-driving vehicles

  Ambiguity in human-oriented traffic laws poses a significant challenge to the regulation of self-driving vehicles. Here, the authors present a trigger-based hierarchical online compliance monitor for self-assessment of self-driving vehicles using ambiguous compliance threshold selection principles.

  • Wenhao Yu
  • Chengxiang Zhao
  • Ding Zhao

  ArticleOpen Access9 Jan 2024 Nature Communications
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  Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions

  Heavy traffic jams are difficult to predict due to the complexity of traffic dynamics. The authors propose a framework to unveil identifiable early signals and predict the eventual outcome of traffic bottlenecks, which may be useful for designing effective methods preventing traffic jams.

  • Jinxiao Duan
  • Guanwen Zeng
  • Shlomo Havlin

  ArticleOpen Access4 Dec 2023 Nature Communications
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  Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach

  Device imperfections are a major challenge that limit the potential of analogue neural networks. Nanyang Ye and colleagues propose a training-time noise injection approach to improve their robustness without hardware modifications, which comes with theoretical guarantees.

  • Nanyang Ye
  • Linfeng Cao
  • Guang-Zhong Yang

  ArticleOpen Access11 May 2023 Communications Engineering
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  Learning naturalistic driving environment with statistical realism

  Simulation of naturalistic driving environment for autonomous vehicle development is challenging due to its complexity and high dimensionality. The authors develop a deep learning-based framework to model driving behavior including safety-critical events for improved training of autonomous vehicles.

  • Xintao Yan
  • Zhengxia Zou
  • Henry X. Liu

  ArticleOpen Access11 Apr 2023 Nature Communications
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  Haptics based multi-level collaborative steering control for automated driving

  Over-reliance on automation in transportation systems is known to cause accidents. To address this, here, Tomohiro Nakade and colleagues describe a collaborative strategy for autonomous steering, in which the driver can take over from the automation without its full deactivation.

  • Tomohiro Nakade
  • Robert Fuchs
  • Jürg Schiffmann

  ArticleOpen Access23 Jan 2023 Communications Engineering