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Found 30 papers, 1 papers with code
Learning to Drive via Asymmetric Self-Play
Large-scale data is crucial for learning realistic and capable driving policies.
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
Perceiving the world and forecasting its future state is a critical task for self-driving.
DeTra: A Unified Model for Object Detection and Trajectory Forecasting
The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving.
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
Instead, we shift the paradigm to have the planner query occupancy at relevant spatio-temporal points, restricting the computation to those regions of interest.
Towards Unsupervised Object Detection From LiDAR Point Clouds
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes.
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Learning world models can teach an agent how the world works in an unsupervised manner.
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Auto-labels are most commonly generated via a two-stage approach -- first objects are detected and tracked over time, and then each object trajectory is passed to a learned refinement model to improve accuracy.
4D-Former: Multimodal 4D Panoptic Segmentation
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time.
Ranked #1 on
Panoptic Tracking
on Panoptic nuScenes val
MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory
To address this challenge, we propose a novel framework for semantic segmentation of a temporal sequence of LiDAR point clouds that utilizes a memory network to store, update and retrieve past information.
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants.
MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation
Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations.
GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting
However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent.
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance.
IntentNet: Learning to Predict Intention from Raw Sensor Data
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants.
MP3: A Unified Model to Map, Perceive, Predict and Plan
High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information.
End-to-end Interpretable Neural Motion Planner
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users.
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning.
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
Diverse Complexity Measures for Dataset Curation in Self-driving
Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
In this paper, we present LookOut, a novel autonomy system that perceives the environment, predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of the SDV by optimizing a set of contingency plans over these future realizations.
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.
StrObe: Streaming Object Detection from LiDAR Packets
In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations.
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity.
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants.
The Importance of Prior Knowledge in Precise Multimodal Prediction
Towards this goal, we design a framework that leverages REINFORCE to incorporate non-differentiable priors over sample trajectories from a probabilistic model, thus optimizing the whole distribution.
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles.
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
A graph neural network then iteratively updates the actor states via a message passing process.
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Particularly difficult is the prediction of human behavior.
