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Found 85 papers, 27 papers with code
Fair Play in the Fast Lane: Integrating Sportsmanship into Autonomous Racing Systems
Autonomous racing has gained significant attention as a platform for high-speed decision-making and motion control.
DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving
To this end, we introduce the Large 4D Gaussian Reconstruction Model (DrivingRecon), a generalizable driving scene reconstruction model, which directly predicts 4D Gaussian from surround view videos.
Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving
Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception.
DexDiffuser: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation
Our framework achieves an average of 70. 7% success rate on goal adaptive dexterous tasks, highlighting its robustness and flexibility in contact-rich manipulation.
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarios
Recent advancements have exploited diffusion models for the synthesis of either LiDAR point clouds or camera image data in driving scenarios.
CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians
To achieve this goal, two core designs are proposed: (1) 3D Gaussians Initialization with 2D compositionality: We transfer the well-established 2D compositionality to initialize the Gaussian parameters on an entity-by-entity basis, ensuring both consistent 3D priors for each entity and reasonable interactions among multiple entities; (2) Dynamic Optimization: We propose a dynamic strategy to optimize 3D Gaussians using Score Distillation Sampling (SDS) loss.
PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views
We propose PixelGaussian, an efficient feed-forward framework for learning generalizable 3D Gaussian reconstruction from arbitrary views.
TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection
Semi-supervised 3D object detection is a common strategy employed to circumvent the challenge of manually labeling large-scale autonomous driving perception datasets.
DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation
It consists of a pyramid structure with a spatial information reuse strategy, a sequential pose initialization module, a gated hierarchical pose refinement module, and a temporal feature propagation module.
Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning
The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning.
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention
Instead of inferring the complete human behavior characteristics, MEReQ infers a residual reward function that captures the discrepancy between the human expert's and the prior policy's underlying reward functions.
$\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving
Photorealistic 3D reconstruction of street scenes is a critical technique for developing real-world simulators for autonomous driving.
NeRF in Robotics: A Survey
Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields.
Q-SLAM: Quadric Representations for Monocular SLAM
In this paper, we reimagine volumetric representations through the lens of quadrics.
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians.
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
With these two capabilities, PhyGrasp is able to accurately assess the physical properties of object parts and determine optimal grasping poses.
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving
Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames.
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay
Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL.
Depth-aware Volume Attention for Texture-less Stereo Matching
Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models.
SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries
Furthermore, we propose novel guidance objectives and a partial diffusion process that enables users to control key aspects of the scenarios, such as the collision type and aggressiveness of the adversarial agent, while maintaining the realism of the behavior.
Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing
In the exploration, a MPC collects diverse data by balancing the racing objectives and the exploration criterion; then the GP is re-trained.
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization
We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments.
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability.
Human-oriented Representation Learning for Robotic Manipulation
We introduce Task Fusion Decoder as a plug-and-play embedding translator that utilizes the underlying relationships among these perceptual skills to guide the representation learning towards encoding meaningful structure for what's important for all perceptual skills, ultimately empowering learning of downstream robotic manipulation tasks.
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving
This paper addresses the growing demands for safety and comfort in intelligent robot systems, particularly autonomous vehicles, where road conditions play a pivotal role in overall driving performance.
Towards Free Data Selection with General-Purpose Models
However, current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly.
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms.
Pre-training on Synthetic Driving Data for Trajectory Prediction
Accumulating substantial volumes of real-world driving data proves pivotal in the realm of trajectory forecasting for autonomous driving.
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation.
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input.
An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training
We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently.
Residual Q-Learning: Offline and Online Policy Customization without Value
Specifically, we formulate the customization problem as a Markov Decision Process (MDP) with a reward function that combines 1) the inherent reward of the demonstration; and 2) the add-on reward specified by the downstream task.
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning
Our Skill-Critic algorithm optimizes both the low-level and high-level policies; these policies are initialized and regularized by the latent space learned from offline demonstrations to guide the parallel policy optimization.
Doubly Robust Self-Training
Self-training is an important technique for solving semi-supervised learning problems.
Quadric Representations for LiDAR Odometry, Mapping and Localization
Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes and achieve high accuracy in autonomous driving tasks.
SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection
However, given that objects occupy only a small part of a scene, finding dense candidates and generating dense representations is noisy and inefficient.
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space.
Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation
To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment.
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49. 7% recall@1 with 2. 2% model parameters, outperforming the latest competitors by 2. 0%.
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception.
Ranked #1 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects
In this work, we propose a novel approach Center Feature Fusion (CFF), in which we leverage center-based detection networks in both the camera and LiDAR streams to identify relevant object locations.
Analyzing and Enhancing Closed-loop Stability in Reactive Simulation
In this paper, we aim to provide a thorough stability analysis of the reactive simulation and propose a solution to enhance the stability.
Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation
Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems.
What Matters for 3D Scene Flow Network
Our proposed model surpasses all existing methods by at least 38. 2% on FlyingThings3D dataset and 24. 7% on KITTI Scene Flow dataset for EPE3D metric.
SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera
With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics.
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e. g., 34K samples in the nuScenes dataset).
Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction
Moreover, to properly evaluate an IBP model with offline datasets, we propose a Shapley-value-based metric to verify if the prediction model satisfies the inherent temporal independence of an interventional distribution.
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion.
Transferable and Adaptable Driving Behavior Prediction
By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.
Towards General and Efficient Active Learning
Existing work follows a cumbersome pipeline that repeats the time-consuming model training and batch data selection multiple times.
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction
In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed.
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling
In this work, we argue that one of the typical formulations of VAEs in multi-agent modeling suffers from an issue we refer to as social posterior collapse, i. e., the model is prone to ignoring historical social context when predicting the future trajectory of an agent.
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning
Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment.
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning
To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN).
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds.
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving.
Diverse Critical Interaction Generation for Planning and Planner Evaluation
Experiments show that our model can generate diverse interactions in various scenarios.
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers.
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #5 on
2D Object Detection
on CeyMo
IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data
We find three interpretable patterns of interactions, bringing insights for driver behavior representation, modeling and comprehension.
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties.
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
Different from existing IRL algorithms, by introducing an efficient continuous-domain trajectory sampler, the proposed algorithm can directly learn the reward functions in the continuous domain while considering the uncertainties in demonstrated trajectories from human drivers.
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction.
Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles.
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image.
Ranked #26 on
3D Semantic Segmentation
on SemanticKITTI
Inferring Spatial Uncertainty in Object Detection
Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
AutoScale: Learning to Scale for Crowd Counting and Localization
A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field.
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants.
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving
The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method.
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
Hence, the goal of this work is to formulate the human drivers' behavior generation model with CPT so that some ``irrational'' behavior or decisions of human can be better captured and predicted.
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling
In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task.
Behavior Planning of Autonomous Cars with Social Perception
The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area.
Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model
The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents.
A Framework for Probabilistic Generic Traffic Scene Prediction
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles.
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.
Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning
To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly.
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real
Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making.
Courteous Autonomous Cars
Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly.
Probabilistic Prediction of Vehicle Semantic Intention and Motion
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles.
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
The fusion method shows particular benefit for detection of pedestrians in the bird view compared to other fusion-based object detection networks.
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility.
