Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline

• URL: http://arxiv.org/abs/2206.08129v1
• Date: Thu, 16 Jun 2022 12:42:44 GMT
• Title: Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
• Authors: Penghao Wu, Xiaosong Jia, Li Chen, Junchi Yan, Hongyang Li, Yu Qiao
• Abstract summary: Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly. Our integrated approach has two branches for trajectory planning and direct control, respectively. Results are evaluated in the closed-loop urban driving setting with challenging scenarios using the CARLA simulator.
• Score: 96.31941517446859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Specifically, our integrated approach has two branches for trajectory planning and direct control, respectively. The trajectory branch predicts the future trajectory, while the control branch involves a novel multi-step prediction scheme such that the relationship between current actions and future states can be reasoned. The two branches are connected so that the control branch receives corresponding guidance from the trajectory branch at each time step. The outputs from two branches are then fused to achieve complementary advantages. Our results are evaluated in the closed-loop urban driving setting with challenging scenarios using the CARLA simulator. Even with a monocular camera input, the proposed approach ranks $first$ on the official CARLA Leaderboard, outperforming other complex candidates with multiple sensors or fusion mechanisms by a large margin. The source code and data will be made publicly available at https://github.com/OpenPerceptionX/TCP.

Related papers

• DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving [81.04174379726251]
  This paper collects a comprehensive end-to-end driving dataset named DriveCoT. It contains sensor data, control decisions, and chain-of-thought labels to indicate the reasoning process. We propose a baseline model called DriveCoT-Agent, trained on our dataset, to generate chain-of-thought predictions and final decisions.
  arXiv  Detail & Related papers  (2024-03-25T17:59:01Z)
• PBP: Path-based Trajectory Prediction for Autonomous Driving [3.640190809479174]
  Trajectory prediction plays a crucial role in the autonomous driving stack. Goal-based prediction models have gained traction in recent years for addressing the multimodal nature of future trajectories.
  arXiv  Detail & Related papers  (2023-09-07T14:45:41Z)
• End-to-end Autonomous Driving: Challenges and Frontiers [45.391430626264764]
  We provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. We discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework.
  arXiv  Detail & Related papers  (2023-06-29T14:17:24Z)
• Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision [2.365702128814616]
  Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving. We propose a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments.
  arXiv  Detail & Related papers  (2023-06-06T02:46:28Z)
• Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information [50.40632021583213]
  Traditional approaches to prediction of future trajectory of road agents rely on knowing information about their past trajectory. This work instead relies on having knowledge of the current state and intended direction to make predictions for multiple vehicles at intersections. Message passing of this information between the vehicles provides each one of them a more holistic overview of the environment.
  arXiv  Detail & Related papers  (2023-01-06T15:13:23Z)
• Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot [112.21008828205409]
  Comma.ai claims one $999 aftermarket device mounted with a single camera and board inside owns the ability to handle L2 scenarios. Together with open-sourced software of the entire system released by Comma.ai, the project is named Openpilot. In this report, we would like to share our latest findings, shed some light on the new perspective of end-to-end autonomous driving from an industrial product-level side.
  arXiv  Detail & Related papers  (2022-06-16T13:43:52Z)
• Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
  We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
  arXiv  Detail & Related papers  (2022-04-28T07:37:21Z)
• Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction [71.97877759413272]
  Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. Our work addresses two key challenges in trajectory prediction, learning outputs, and better predictions by imposing constraints using driving knowledge.
  arXiv  Detail & Related papers  (2021-04-16T17:58:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.