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Thread: Miscellaneous

  1. #191

    Gazing Car

    Sep 20, 2022

    Article "Animated Googly Eyes Could Make Autonomous Cars Safer For Pedestrians"
    A hilarious upgrade makes it obvious what a self-driving car has detected.

    by Andrew Liszewski
    October 14, 2022

  2. #192
    Article "Slow Self-Driving Car Progress Tests Investors’ Patience"
    Autonomous-vehicle industry faces more skepticism as it struggles to deploy robot drivers

    by Tim Higgins
    November 28, 2022

  3. #193

    Top Robocar Stories of 2022 from

    Jan 9, 2023

    The annual summary of the big stories in self-driving: A year with lots of good and bad news about Argo, Waymo, Cruise, China, Delivery and more.

    0:00 - Intro
    0:54 - The bad news
    7:50 - There's good news
    12:30 - Not so ugly
    "Top Self-Driving Car Stories Of 2022 In Review - Big Ups, Big Downs"

    by Brad Templeton
    January 9, 2023
    Last edited by Airicist2; 16th January 2023 at 10:22.

  4. #194
    Article "Will We Blame Self-Driving Cars?"
    A new study finds that people are likely to hold autonomous vehicles liable for accidents even when they’re not at fault.

    by Julian de Freitas
    January 26, 2023

  5. #195

  6. #196

    Detecting AV failures in MIT’s MiniCity

    Mar 21, 2023

    Infrastructure-based End-to-End Learning and Prevention of Driver Failure
    Authors: Noam Buckman, Shiva Sreeram, Mathias Lechner, Yutong Ban, Ramin Hasani, Sertac Karaman, Daniela Rus
    Conference: ICRA 2023
    Sponsors: Toyota Research Institute

  7. #197

    Developing safe autonomous vehicles

    Mar 22, 2023

    Uncertainty about self-driving cars / autonomous vehicles (AVs) is at an all-time high. Michigan Engineering researchers aim to change that. Training AVs to recognize safety hazards is a complicated task. Autonomous vehicles can typically handle 99.99% of safety use cases. Once you get to the 0.001%, AVs may not be able to handle these case uses because they haven’t seen the scenarios yet. This 0.001% is the curse of rarity. Training autonomous vehicle software is especially time-consuming and expensive, because individual safety use cases come up so rarely in normal driving conditions.

    To fix this problem, a team of researchers used artificial intelligence to train virtual vehicles that can challenge autonomous vehicles in a virtual or augmented reality testing environment. The virtual cars were only fed safety-critical training data, making them better equipped to challenge AVs with more of those rare events in a shorter amount of time. In an era of uncertainty towards AVs, this solution can save auto manufacturers a prohibitive amount of time and money to ensure their systems are safe.

    This research was led by Professor Henry Liu, Director of Center for Connected and Automated Transportation (CCAT), Director of Mcity

    "Dense reinforcement learning for safety validation of autonomous vehicles"
    Journal: Nature
    Date: March 22, 2023
    DOI: 10.1038/s41586-023-05732-2

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