Search Results for author:
Found 35 papers, 14 papers with code
A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential
In this paper, we present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar.
Object-centric Cross-modal Feature Distillation for Event-based Object Detection
In this paper, we develop a novel knowledge distillation approach to shrink the performance gap between these two modalities.
er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds
The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars.
U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization
Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails.
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding traffic participants.
Prior Based Online Lane Graph Extraction from Single Onboard Camera Image
Thus, online estimation of the lane graph is crucial for widespread and reliable autonomous navigation.
Improving Online Lane Graph Extraction by Object-Lane Clustering
In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection outputs.
Online Lane Graph Extraction from Onboard Video
One of the most common and useful representation of such an understanding is done in the form of BEV lane graphs.
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving, and based on TrafficBots we obtain a world model tailored for the planning module of autonomous vehicles.
A Multiplicative Value Function for Safe and Efficient Reinforcement Learning
To this end, we propose a safe model-free RL algorithm with a novel multiplicative value function consisting of a safety critic and a reward critic.
Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images
On the one hand, the proposed method learns to segment these planar hulls from the labeled data.
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields
In this paper, we tackle the problem of active robotic 3D reconstruction of an object.
Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds
This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 $m/s$.
Deep Gradient Learning for Efficient Camouflaged Object Detection
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD).
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
We focus on the supervised setup, in which ground-truth depth is available only at training time.
Ranked #6 on
Depth Estimation
on NYU-Depth V2
Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models
Fundamental to the success of our method is the design of a novel multi-agent policy network that can steer a vehicle given the state of the surrounding agents and the map information.
Adiabatic Quantum Computing for Multi Object Tracking
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
End-to-End Learning of Multi-category 3D Pose and Shape Estimation
We use a Transformer-based architecture to detect the keypoints, as well as to summarize the visual context of the image.
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points.
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
Ranked #3 on
Lane Detection
on nuScenes
End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense benchmark and state-of-the-art performance on the challenging public routes of the CARLA LeaderBoard.
Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction
Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems.
Learnable Online Graph Representations for 3D Multi-Object Tracking
Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality.
Spectral Tensor Train Parameterization of Deep Learning Layers
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context.
Understanding Bird's-Eye View of Road Semantics using an Onboard Camera
In this work, we study scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera.
Learning from Simulation, Racing in Reality
We present a reinforcement learning-based solution to autonomously race on a miniature race car platform.
Learning Accurate and Human-Like Driving using Semantic Maps and Attention
This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like.
Competitive Policy Optimization
A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties.
Safe Motion Planning for Autonomous Driving using an Adversarial Road Model
This formulation allows us to compute safe sets using tools from viability theory, that can be used as terminal constraints in an optimization-based motion planner.
Robotics Systems and Control Systems and Control Optimization and Control
Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context
Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228, 000 action sequences.
Quantifying Data Augmentation for LiDAR based 3D Object Detection
In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection.
Learning a Curve Guardian for Motorcycles
Our contributes are fourfold: 1) we predict the motorcycle's intra-lane position using a convolutional neural network (CNN), 2) we predict the motorcycle roll angle using a CNN, 3) we use an upgraded controller model that incorporates road incline for a more realistic model and prediction, 4) we design a scale-able system by utilizing HERE Technologies map database to obtain the accurate road geometry of the future path.
AMZ Driverless: The Full Autonomous Racing System
This paper presents the algorithms and system architecture of an autonomous racecar.
Robotics
Optimization-Based Autonomous Racing of 1:43 Scale RC Cars
This paper describes autonomous racing of RC race cars based on mathematical optimization.
Optimization and Control Robotics Systems and Control
Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars
This paper presents an adaptive high performance control method for autonomous miniature race cars.
