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Found 19 papers, 13 papers with code
Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions
Simulating such safety-critical scenarios is nontrivial, however, from log data that are typically regular scenarios, especially in consideration of dynamic adversarial interactions between the future motions of autonomous vehicles and surrounding traffic participants.
LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking
The system consists of an Analytic Process (System-II) that accumulates driving experience through logical reasoning and a Heuristic Process (System-I) that refines this knowledge via fine-tuning and few-shot learning.
DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenes
Recent advances in diffusion models have improved controllable streetscape generation and supported downstream perception and planning tasks.
LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Camera
In this paper, we focus on the potential of 3D radar in semantic scene completion, pioneering cross-modal refinement techniques for improved robustness against weather and illumination changes, and enhancing SSC performance. Regarding model architecture, we propose a three-stage tight fusion approach on BEV to realize a fusion framework for point clouds and images.
KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models
To broaden the horizons of knowledge-driven strategies and bolster the generalization capabilities of autonomous agents, we propose the KoMA framework consisting of multi-agent interaction, multi-step planning, shared-memory, and ranking-based reflection modules to enhance multi-agents' decision-making in complex driving scenarios.
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset.
Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving
Experiments also demonstrate that as the memory bank expands, the Heuristic Process with only 1. 8B parameters can inherit the knowledge from a GPT-4 powered Analytic Process and achieve continuous performance improvement.
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Compared to both open-source and proprietary models, InternVL 1. 5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks.
Ranked #6 on
Multiple-choice
on Neptune-Full
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving
With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering.
Towards Knowledge-driven Autonomous Driving
This paper explores the emerging knowledge-driven autonomous driving technologies.
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving.
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability.
Ranked #1 on
10-shot image generation
on FQL-Driving
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
In this paper, we explore the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize when facing complex scenarios.
LimSim: A Long-term Interactive Multi-scenario Traffic Simulator
With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing.
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations
In this work, we propose a new imitation learning approach called Hierarchical Imitation Learning from Observation(HILONet), which adopts a hierarchical structure to choose feasible sub-goals from demonstrated observations dynamically.
Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together
We also deploy and validate our method in a real world scenario.
Robotics Multiagent Systems
CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints
In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem.
Robotics Multiagent Systems
Collision-free Trajectory Planning for Autonomous Surface Vehicle
We apply a numerical optimization method in the back-end to generate the trajectory.
Robotics
ZJUNlict Extended Team Description Paper for RoboCup 2019
For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion and therefore, this paper thoroughly described the devotion which ZJUNLict has devoted and the effort that ZJUNLict has contributed.
Robotics 68T40
