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  • Trajectory-guided Control Prediction for End-to-end Autonomous Driving . . .
    Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly, which have spanned two separately studied lines of research Seeing their potential mutual benefits to each other, this paper takes the initiative to explore the combination of these two well-developed worlds
  • 端到端自动驾驶中轨迹引导的控制预测：一个简单有力的基线 . . .
    arXiv论文“Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline“， 2022年6月，上海AI实验室和上海交大。 当前的端到端自主驾驶方法要么基于规划轨迹运行控…
  • 【论文阅读】Trajectory-guided Control Prediction for End-to . . .
    Summary: leaderboard 现存第一名 TCP，非常simple的设置 取得了很好的效果 论文链接： Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline 代码链接： https: github com OpenPerceptionX TCP 作者回复预计9月开 jg刷到的时候 我旁边看了一眼 这些框架图为什么这么熟… 后面仔细看才发现喔 吸取各家之长处了 简单传感器的高分 真绝啊 1 Motivation
  • TCP - Trajectory-guided Control Prediction for End-to-end Autonomous . . .
    TCP is a simple unified framework to combine trajectory and control prediction for end-to-end autonomous driving By time of release in June 17 2022, our method achieves new state-of-the-art on CARLA AD Leaderboard, in which we rank the first in terms of the Driving Score and Infraction Penalty using only a single camera as input
  • Trajectory-guided Control Prediction for End-to-end . . .
    端到端自动驾驶方法将原始传感器数据直接映射到规划的轨迹或低级控制动作，显示出简单的优点，在概念上避免了复杂的模块化设计和繁重的手工规则的级联错误。 端到端自动驾驶模型的输出预测通常分为两种形式：轨迹 路线点 [48, 4, 11, 46, 15, 29, 10]和直接控制动作 [17, 39, 18, 42, 12, 60, 9]。 然而，对于这两种形式中的哪一种更适合所有情况或某些场景，目前还没有明确的结论。 与可直接应用于车辆的控制预测不同，对于规划轨迹的方法，通常需要额外的控制器，例如PID控制器，作为将规划轨迹转换为控制信号的后续步骤。
  • Trajectory-guided control prediction for end-to-end autonomous driving . . .
    Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly, which have spanned two separately studied lines of research Seeing their potential mutual benefits to each other, this paper takes the initiative to explore the combination of these two well-developed worlds
  • Trajectory-guided Control Prediction for End-to-end Autonomous Driving . . .
    Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly, which have spanned two separately studied lines of research Seeing their potential mutual benefits to each other, this paper takes the initiative to explore the combination of these two well-developed worlds
  • TCP：结合轨迹规划和控制预测的端到端自动驾驶框架 - 知乎
    我们提出了一个统一了两种典型输出形式（轨迹规划和控制预测）的端到端自动驾驶（E2E AD）模型 - TCP （Trajectory-guided Control Prediction），仅使用单一单目相机作为输入，便取得了75 14 Driving Score 和 0 87 Infraction Penalty，在CARLA Autonomous
  • Trajectory-guided_Control_Prediction_for_End-to-end . . .
    同时，为了提高融合关键静态信息(如路沿和车道)的能力，并增强两个分支的空间一致性，我们建议使用Trajectory planning branch来引导Control在每个未来时间步关注输入图像的正确区域
  • ADSAP: An Adaptive Speed-Aware Trajectory Prediction Framework with . . .
    Accurate trajectory prediction of surrounding vehicles is a fundamental challenge in autonomous driving, requiring sophisticated modeling of complex vehicle interactions, traffic dynamics, and contextual dependencies This paper introduces Adaptive Speed-Aware Prediction (ADSAP), a novel trajectory prediction framework that advances the state of the art through innovative mechanisms for





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