82 citations · 170 across the 20 of their papers we have counts for
22 papers
World Engine: Towards the Era of Post-Training for Autonomous Driving
Tianyu Li, Li Chen, Caojun Wang +16
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their…
Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Haochen Liu, Tianyu Li, Haohan Yang +7
End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, exist…
ReSim: Reliable World Simulation for Autonomous Driving
Jiazhi Yang, Kashyap Chitta, Shenyuan Gao +7
How can we reliably simulate future driving scenarios under a wide range of ego driving behaviors? Recent driving world models, developed exclusively on real-world driving data com…
Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving
Haochen Liu, Li Chen, Yu Qiao +2
Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent sce…
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
Shenyuan Gao, Jiazhi Yang, Li Chen +5
World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitati…
GenAD: Generalized Predictive Model for Autonomous Driving
Jiazhi Yang, Shenyuan Gao, Yihang Qiu +11
In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower…
Visual Point Cloud Forecasting enables Scalable Autonomous Driving
Zetong Yang, Li Chen, Yanan Sun +1
In contrast to extensive studies on general vision, pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require feat…
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving
Tianyu Li, Peijin Jia, Bangjun Wang +4
A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map l…
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
Xiaosong Jia, Yulu Gao, Li Chen +3
End-to-end autonomous driving aims to build a fully differentiable system that takes raw sensor data as inputs and directly outputs the planned trajectory or control signals of the…
Scene as Occupancy
Chonghao Sima, Wenwen Tong, Tai Wang +8
Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-a…
End-to-end Autonomous Driving: Challenges and Frontiers
Li Chen, Penghao Wu, Kashyap Chitta +3
The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion pl…
Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
Xiaosong Jia, Penghao Wu, Li Chen +4
End-to-end autonomous driving has made impressive progress in recent years. Existing methods usually adopt the decoupled encoder-decoder paradigm, where the encoder extracts hidden…
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Huijie Wang, Tianyu Li, Yang Li +13
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scen…
Graph-based Topology Reasoning for Driving Scenes
Tianyu Li, Li Chen, Huijie Wang +10
Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assig…
Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling
Penghao Wu, Li Chen, Hongyang Li +3
Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea c…
Planning-oriented Autonomous Driving
Yihan Hu, Jiazhi Yang, Li Chen +13
Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks an…
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
Hongyang Li, Chonghao Sima, Jifeng Dai +19
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches…
ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning
Shengchao Hu, Li Chen, Penghao Wu +3
Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of tasks. To better predict the control signals and enhance user safety, an end-to-end approach t…
Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot
Li Chen, Tutian Tang, Zhitian Cai +6
Equipped with a wide span of sensors, predominant autonomous driving solutions are becoming more modular-oriented for safe system design. Though these sensors have laid a solid fou…
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
Penghao Wu, Xiaosong Jia, Li Chen +3
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 studie…
HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding
Xiaosong Jia, Penghao Wu, Li Chen +3
Encoding a driving scene into vector representations has been an essential task for autonomous driving that can benefit downstream tasks e.g. trajectory prediction. The driving sce…
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Li Chen, Chonghao Sima, Yang Li +8
Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.). Previo…