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<!DOCTYPE html>
<html>
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        content="CarDreamer.">
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          <h1 class="title is-1 publication-title">CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              Dechen Gao<sup>*1</sup>,</span>
            <span class="author-block">
              Shuangyu Cai<sup>*1,2</sup>,</span>
            <span class="author-block">
              Hanchu Zhou<sup>*1</sup>,</span>
            <span class="author-block">
              Hang Wang<sup>*1</sup>,</span>
            <span class="author-block">
              Iman Soltani<sup>1</sup>,</span>
            <span class="author-block">
              Junshan Zhang<sup>1</sup>,</span>


          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>University of California, Davis,</span>
            <span class="author-block"><sup>2</sup>Tsinghua University</span>
          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>*</sup>Equal contribution</span>
          </div>

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                  <span>Model</span>
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</body>

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        <h2 class="title is-3">Abstract</h2>
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          <p>
            To safely navigate intricate real-world scenarios, autonomous vehicles (AVs) must be able to adapt to diverse road conditions and anticipate future events. World model based reinforcement learning (RL) has emerged as a promising approach by learning and predicting the complex dynamics of various environments. Nevertheless, to the best of our knowledge,  there  does not exist an open-source platform for   training and testing such algorithms in complicated driving environments.
          </p>
          <p>
            To fill this void, we introduce CarDreamer, the first open-source learning platform designed specifically for developing and evaluating world model based autonomous driving algorithms. It comprises a few key components, including
            1) World model (WM) backbone: CarDreamer has integrated some state-of-the-art world models, which simplifies the reproduction of RL algorithms.
            2) Built-in tasks:  CarDreamer offers a comprehensive set of highly configurable driving tasks which are compatible with Gym interfaces and are equipped with empirically optimized reward functions.
            3) Task development suite: CarDreamer integrates a flexible task development suite to streamline the creation of driving tasks. This suite enables easy definition of traffic flows and vehicle routes, along with automatic collection of multi-modal observation data.
            Furthermore, we conduct extensive experiments using built-in tasks to evaluate the performance and potential of WMs in autonomous driving.
            Thanks to the richness and flexibility of CarDreamer, we also systematically study the impact of observation modality, observability, and sharing of vehicle intentions on AV safety and efficiency. All code and documents are accessible on our GitHub page.
          </p>
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          <p>
            World model "imagines" the future states based on current and history information. This offers the world model with precious prediction and high sample efficiency.
          </p>
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          <figure>
            <img src="./static/gifs/right_turn_hard_pre_bev.gif" alt="Description of the image" class="imagination-image">
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</section>

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        <h2 class="title is-3">Built-in Tasks</h2>
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          <p>
            CarDreamer provides a comprehensive set of driving tasks, such as lane changing and overtaking. These tasks allow extensive customization in terms of difficulty, observability, observation modalities, and communication of vehicle intentions.
          </p>
        </div>
      </div>
    </div>
  </div>
</section>

<section class="hero teaser">
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          <h3 class="subtitle" style="font-weight: bold;">BEV</h3>
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          <h3 class="subtitle" style="font-weight: bold;">Camera</h3>
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          <h3 class="subtitle">Right turn hard</h3>
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          <h3 class="subtitle">Right turn med.</h3>
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          <h3 class="subtitle">Overtake</h3>
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            <img src="./static/gifs/camera/roundabout.gif" alt="GIF 10">
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          <h3 class="subtitle">Roundabout</h3>
        </div>
      </div>
    </div>
  </div>
</section>

<section class="hero teaser">
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          <h3 class="subtitle" style="font-weight: bold;">BEV</h3>
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        <!-- Column 1 -->
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            <img src="./static/gifs/bev/left_turn_hard.gif" alt="GIF 1">
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          <h3 class="subtitle" style="font-weight: bold;">Camera</h3>
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          <h3 class="subtitle">Left turn hard</h3>
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        <!-- Column 7 -->
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          <figure class="image is-4by4">
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          <h3 class="subtitle">Left turn med.</h3>
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        <!-- Column 8 -->
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          <figure class="image is-4by4">
            <img src="./static/gifs/camera/left_turn_simple.gif" alt="GIF 8">
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          <h3 class="subtitle">Left turn simple</h3>
        </div>
        <!-- Column 9 -->
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          <figure class="image is-4by4">
            <img src="./static/gifs/camera/four_lane.gif" alt="GIF 9">
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          <h3 class="subtitle">Four lane</h3>
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        <!-- Column 10 -->
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          <h3 class="subtitle">Navigation</h3>
        </div>
      </div>
    </div>
  </div>
</section>


<section class="hero teaser">
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          <h3 class="subtitle" style="font-weight: bold;">BEV</h3>
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            <img src="./static/gifs/bev/lane_merge.gif" alt="GIF 5">
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      <!-- Second Row -->
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          <h3 class="subtitle" style="font-weight: bold;">Camera</h3>
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          <h3 class="subtitle">Traffic light</h3>
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          </figure>
          <h3 class="subtitle">Traffic light</h3>
        </div>
        <!-- Column 8 -->
        <div class="column is-one-fifth has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/camera/tl_straight.gif" alt="GIF 8">
          </figure>
          <h3 class="subtitle">Traffic light</h3>
        </div>
        <!-- Column 9 -->
        <div class="column is-one-fifth has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/camera/Stop sign.gif" alt="GIF 9">
          </figure>
          <h3 class="subtitle">Stop sign</h3>
        </div>
        <!-- Column 10 -->
        <div class="column is-one-fifth has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/camera/lane_merge.gif" alt="GIF 10">
          </figure>
          <h3 class="subtitle">Lane merge</h3>
        </div>
      </div>
    </div>
  </div>
</section>

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    <div class="hero-body">
      <!-- First Row -->
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        <!-- Text for First Row -->
        <div class="column is-narrow has-text-centered text-column">
          <h3 class="subtitle" style="font-weight: bold;">BEV</h3>
        </div>
        <!-- Column 1 -->
        <div class="column is-one-third has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/bev/right_turn_raw_fail.gif" alt="GIF 1">
          </figure>
        </div>
        <!-- Column 2 -->
        <div class="column is-one-third has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/bev/right_turn_fov.gif" alt="GIF 2">
          </figure>
        </div>
        <!-- Column 3 -->
        <div class="column is-one-third has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/bev/right_turn_sfov.gif" alt="GIF 3">
          </figure>
        </div>
      </div>

      <!-- Second Row -->
      <div class="columns is-centered">
        <!-- Text for Second Row -->
        <div class="column is-narrow has-text-centered text-column">
          <h3 class="subtitle" style="font-weight: bold;">Camera</h3>
        </div>
        <!-- Column 6 -->
        <div class="column is-one-third has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs//camera/right_turn_raw_fail.gif" alt="GIF 6">
          </figure>
          <h3 class="subtitle">Right turn raw</h3>
        </div>
        <!-- Column 7 -->
        <div class="column is-one-third has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/camera/right_turn_fov.gif" alt="GIF 7">
          </figure>
          <h3 class="subtitle">Right turn FOV</h3>
        </div>
        <!-- Column 8 -->
        <div class="column is-one-third has-text-centered">
          <figure class="image is-4by4">
            <img src="./static/gifs/camera/right_turn_sfov.gif" alt="GIF 8">
          </figure>
          <h3 class="subtitle">Right turn SFOV</h3>
        </div>
      </div>
    </div>
  </div>
</section>

<section class="section">
  <div class="container is-max-desktop">
    <div class="columns is-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Task Performance</h2>
        <div class="content has-text-justified">
          <p>This table illustrates the performance metrics such as success rate, collision rate, and average speed across different driving tasks.</p>
        </div>
        <div class="image-container">
          <figure>
            <img src="./static/images/tables/task_performance.png" alt="Task Performance Table" class="responsive-image">
            <figcaption class="caption">Table: Task Performance Metrics</figcaption>
          </figure>
        </div>
      </div>
    </div>
  </div>
</section>

<section class="section">
  <div class="container is-max-desktop">
    <div class="columns is-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Observability Settings</h2>
        <div class="content has-text-justified">
          <p>This table shows the effect of different observability settings (FOV, SFOV, Full) on task success, collision rates, and speed.</p>
        </div>
        <div class="image-container">
          <figure>
            <img src="./static/images/tables/observability_performance.png" alt="Observability Performance Table" class="responsive-image">
            <figcaption class="caption">Table: Observability Performance Metrics</figcaption>
          </figure>
        </div>
      </div>
    </div>
  </div>
</section>

<section class="section">
  <div class="container is-max-desktop">
    <div class="columns is-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Intention Sharing and Transmission Errors</h2>
        <div class="content has-text-justified">
          <p>This table compares how different levels of transmission error affect the agent’s success rate, collision rate, and average speed when sharing vehicle intentions.</p>
        </div>
        <div class="image-container">
          <figure>
            <img src="./static/images/tables/transmission_error_intention.png" alt="Transmission Error Intention Table" class="responsive-image">
            <figcaption class="caption">Table: Transmission Error vs. Intention Sharing</figcaption>
          </figure>
        </div>
      </div>
    </div>
  </div>
</section>


<!--/ Visulization Tool. -->
<section class="section">
  <div class="container is-max-desktop">
    <div class="columns is-centered has-text-centered">
      <div class="column is-full-width">
        <h2 class="title is-3">Visulization Tool</h2>
        <div class="content has-text-justified">
          <p>
            We stream observations, rewards, terminal conditions, and custom metrics to a web browser for each training session in real-time, making it easier to engineer rewards and debug.
          </p>
        </div>
        <div class="image-container">
          <figure>
            <img src="./static/images/visualization.png" alt="Description of the image" class="imagination-image" width="100%" height="auto">
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</section>

<section class="section" id="IEEE">
  <div class="container is-max-desktop content">
    <h2 class="title">IEEE Citation</h2>
    <pre><code>@ARTICLE{10714437,
      author={Gao, Dechen and Cai, Shuangyu and Zhou, Hanchu and Wang, Hang and Soltani, Iman and Zhang, Junshan},
      journal={IEEE Internet of Things Journal},
      title={CarDreamer: Open-Source Learning Platform for World Model Based Autonomous Driving},
      year={2024},
      volume={},
      number={},
      pages={1-1},
      keywords={Autonomous Driving;Reinforcement Learning;World Model},
      doi={10.1109/JIOT.2024.3479088}
    }</code></pre>
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