Rethinking Imitation-based Planner for Autonomous Driving

by   Jie Cheng, et al.

In recent years, imitation-based driving planners have reported considerable success. However, due to the absence of a standardized benchmark, the effectiveness of various designs remains unclear. The newly released nuPlan addresses this issue by offering a large-scale real-world dataset and a standardized closed-loop benchmark for equitable comparisons. Utilizing this platform, we conduct a comprehensive study on two fundamental yet underexplored aspects of imitation-based planners: the essential features for ego planning and the effective data augmentation techniques to reduce compounding errors. Furthermore, we highlight an imitation gap that has been overlooked by current learning systems. Finally, integrating our findings, we propose a strong baseline model-PlanTF. Our results demonstrate that a well-designed, purely imitation-based planner can achieve highly competitive performance compared to state-of-the-art methods involving hand-crafted rules and exhibit superior generalization capabilities in long-tail cases. Our models and benchmarks are publicly available. Project website


Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning

Learning-based approaches have achieved impressive performance for auton...

Rethinking Closed-loop Training for Autonomous Driving

Recent advances in high-fidelity simulators have enabled closed-loop tra...

Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

We demonstrate the first large-scale application of model-based generati...

MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving

To plan a safe and efficient route, an autonomous vehicle should anticip...

Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization

We present a learning-based planner that aims to robustly drive a vehicl...

One-shot Visual Imitation via Attributed Waypoints and Demonstration Augmentation

In this paper, we analyze the behavior of existing techniques and design...

The False Promise of Imitating Proprietary LLMs

An emerging method to cheaply improve a weaker language model is to fine...

Please sign up or login with your details

Forgot password? Click here to reset