4,471 research outputs found
BirdNet: a 3D Object Detection Framework from LiDAR information
Understanding driving situations regardless the conditions of the traffic
scene is a cornerstone on the path towards autonomous vehicles; however,
despite common sensor setups already include complementary devices such as
LiDAR or radar, most of the research on perception systems has traditionally
focused on computer vision. We present a LiDAR-based 3D object detection
pipeline entailing three stages. First, laser information is projected into a
novel cell encoding for bird's eye view projection. Later, both object location
on the plane and its heading are estimated through a convolutional neural
network originally designed for image processing. Finally, 3D oriented
detections are computed in a post-processing phase. Experiments on KITTI
dataset show that the proposed framework achieves state-of-the-art results
among comparable methods. Further tests with different LiDAR sensors in real
scenarios assess the multi-device capabilities of the approach.Comment: Submittied to IEEE International Conference on Intelligent
Transportation Systems 2018 (ITSC
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios
Vehicle detection and tracking is a core ingredient for developing autonomous
driving applications in urban scenarios. Recent image-based Deep Learning (DL)
techniques are obtaining breakthrough results in these perceptive tasks.
However, DL research has not yet advanced much towards processing 3D point
clouds from lidar range-finders. These sensors are very common in autonomous
vehicles since, despite not providing as semantically rich information as
images, their performance is more robust under harsh weather conditions than
vision sensors. In this paper we present a full vehicle detection and tracking
system that works with 3D lidar information only. Our detection step uses a
Convolutional Neural Network (CNN) that receives as input a featured
representation of the 3D information provided by a Velodyne HDL-64 sensor and
returns a per-point classification of whether it belongs to a vehicle or not.
The classified point cloud is then geometrically processed to generate
observations for a multi-object tracking system implemented via a number of
Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position
and velocity of the surrounding vehicles. The system is thoroughly evaluated on
the KITTI tracking dataset, and we show the performance boost provided by our
CNN-based vehicle detector over a standard geometric approach. Our lidar-based
approach uses about a 4% of the data needed for an image-based detector with
similarly competitive results.Comment: Presented in IEEE ECMR 2017. IEEE Copyrights: Personal use of this
material is permitted. Permission from IEEE must be obtained for all other
use
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
Autonomous vehicles may make wrong decisions due to inaccurate detection and
recognition. Therefore, an intelligent vehicle can combine its own data with
that of other vehicles to enhance perceptive ability, and thus improve
detection accuracy and driving safety. However, multi-vehicle cooperative
perception requires the integration of real world scenes and the traffic of raw
sensor data exchange far exceeds the bandwidth of existing vehicular networks.
To the best our knowledge, we are the first to conduct a study on raw-data
level cooperative perception for enhancing the detection ability of
self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse
the sensor data collected from different positions and angles of connected
vehicles. A point cloud based 3D object detection method is proposed to work on
a diversity of aligned point clouds. Experimental results on KITTI and our
collected dataset show that the proposed system outperforms perception by
extending sensing area, improving detection accuracy and promoting augmented
results. Most importantly, we demonstrate it is possible to transmit point
clouds data for cooperative perception via existing vehicular network
technologies.Comment: Accepted by the 39th IEEE International Conference on Distributed
Computing Systems (ICDCS 2019
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Recently, the advancement of deep learning in discriminative feature learning
from 3D LiDAR data has led to rapid development in the field of autonomous
driving. However, automated processing uneven, unstructured, noisy, and massive
3D point clouds is a challenging and tedious task. In this paper, we provide a
systematic review of existing compelling deep learning architectures applied in
LiDAR point clouds, detailing for specific tasks in autonomous driving such as
segmentation, detection, and classification. Although several published
research papers focus on specific topics in computer vision for autonomous
vehicles, to date, no general survey on deep learning applied in LiDAR point
clouds for autonomous vehicles exists. Thus, the goal of this paper is to
narrow the gap in this topic. More than 140 key contributions in the recent
five years are summarized in this survey, including the milestone 3D deep
architectures, the remarkable deep learning applications in 3D semantic
segmentation, object detection, and classification; specific datasets,
evaluation metrics, and the state of the art performance. Finally, we conclude
the remaining challenges and future researches.Comment: 21 pages, submitted to IEEE Transactions on Neural Networks and
Learning System
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps
Lidar has become an essential sensor for autonomous driving as it provides
reliable depth estimation. Lidar is also the primary sensor used in building 3D
maps which can be used even in the case of low-cost systems which do not use
Lidar. Computation on Lidar point clouds is intensive as it requires processing
of millions of points per second. Additionally there are many subsequent tasks
such as clustering, detection, tracking and classification which makes
real-time execution challenging. In this paper, we discuss real-time dynamic
object detection algorithms which leverages previously mapped Lidar point
clouds to reduce processing. The prior 3D maps provide a static background
model and we formulate dynamic object detection as a background subtraction
problem. Computation and modeling challenges in the mapping and online
execution pipeline are described. We propose a rejection cascade architecture
to subtract road regions and other 3D regions separately. We implemented an
initial version of our proposed algorithm and evaluated the accuracy on CARLA
simulator.Comment: Preprint Submission to ECCVW AutoNUE 2018 - v2 author name accent
correctio
Self-Driving Cars: A Survey
We survey research on self-driving cars published in the literature focusing
on autonomous cars developed since the DARPA challenges, which are equipped
with an autonomy system that can be categorized as SAE level 3 or higher. The
architecture of the autonomy system of self-driving cars is typically organized
into the perception system and the decision-making system. The perception
system is generally divided into many subsystems responsible for tasks such as
self-driving-car localization, static obstacles mapping, moving obstacles
detection and tracking, road mapping, traffic signalization detection and
recognition, among others. The decision-making system is commonly partitioned
as well into many subsystems responsible for tasks such as route planning, path
planning, behavior selection, motion planning, and control. In this survey, we
present the typical architecture of the autonomy system of self-driving cars.
We also review research on relevant methods for perception and decision making.
Furthermore, we present a detailed description of the architecture of the
autonomy system of the self-driving car developed at the Universidade Federal
do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile
(IARA). Finally, we list prominent self-driving car research platforms
developed by academia and technology companies, and reported in the media
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning
A key challenge for autonomous driving is safe trajectory planning in
cluttered, urban environments with dynamic obstacles, such as pedestrians,
bicyclists, and other vehicles. A reliable prediction of the future
environment, including the behavior of dynamic agents, would allow planning
algorithms to proactively generate a trajectory in response to a rapidly
changing environment. We present a novel framework that predicts the future
occupancy state of the local environment surrounding an autonomous agent by
learning a motion model from occupancy grid data using a neural network. We
take advantage of the temporal structure of the grid data by utilizing a
convolutional long-short term memory network in the form of the PredNet
architecture. This method is validated on the KITTI dataset and demonstrates
higher accuracy and better predictive power than baseline methods.Comment: 8 pages, updated final draft, accepted into Intelligent
Transportation Systems Conference (ITSC) 201
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
With recent advances in learning algorithms and hardware development,
autonomous cars have shown promise when operating in structured environments
under good driving conditions. However, for complex, cluttered and unseen
environments with high uncertainty, autonomous driving systems still frequently
demonstrate erroneous or unexpected behaviors, that could lead to catastrophic
outcomes. Autonomous vehicles should ideally adapt to driving conditions; while
this can be achieved through multiple routes, it would be beneficial as a first
step to be able to characterize Driveability in some quantified form. To this
end, this paper aims to create a framework for investigating different factors
that can impact driveability. Also, one of the main mechanisms to adapt
autonomous driving systems to any driving condition is to be able to learn and
generalize from representative scenarios. The machine learning algorithms that
currently do so learn predominantly in a supervised manner and consequently
need sufficient data for robust and efficient learning. Therefore, we also
perform a comparative overview of 45 public driving datasets that enable
learning and publish this dataset index at
https://sites.google.com/view/driveability-survey-datasets. Specifically, we
categorize the datasets according to use cases, and highlight the datasets that
capture complicated and hazardous driving conditions which can be better used
for training robust driving models. Furthermore, by discussions of what driving
scenarios are not covered by existing public datasets and what driveability
factors need more investigation and data acquisition, this paper aims to
encourage both targeted dataset collection and the proposal of novel
driveability metrics that enhance the robustness of autonomous cars in adverse
environments
BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
In autonomous driving community, numerous benchmarks have been established to
assist the tasks of 3D/2D object detection, stereo vision, semantic/instance
segmentation. However, the more meaningful dynamic evolution of the surrounding
objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset
platform. To address this, we introduce BLVD, a large-scale 5D semantics
benchmark which does not concentrate on the static detection or
semantic/instance segmentation tasks tackled adequately before. Instead, BLVD
aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking,
5D (4D+interactive) interactive event recognition and intention prediction.
This benchmark will boost the deeper understanding of traffic scenes than ever
before. We totally yield 249,129 3D annotations, 4,902 independent individuals
for tracking with the length of overall 214,922 points, 6,004 valid fragments
for 5D interactive event recognition, and 4,900 individuals for 5D intention
prediction. These tasks are contained in four kinds of scenarios depending on
the object density (low and high) and light conditions (daytime and nighttime).
The benchmark can be downloaded from our project site
https://github.com/VCCIV/BLVD/.Comment: To appear in ICRA201
Autonomous Driving without a Burden: View from Outside with Elevated LiDAR
The current autonomous driving architecture places a heavy burden in signal
processing for the graphics processing units (GPUs) in the car. This directly
translates into battery drain and lower energy efficiency, crucial factors in
electric vehicles. This is due to the high bit rate of the captured video and
other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor
at the top of the car which is an essential feature in autonomous vehicles.
LiDAR is needed to obtain a high precision map for the vehicle AI to make
relevant decisions. However, this is still a quite restricted view from the
car. This is the same even in the case of cars without a LiDAR such as Tesla.
The existing LiDARs and the cameras have limited horizontal and vertical fields
of visions. In all cases it can be argued that precision is lower, given the
smaller map generated. This also results in the accumulation of a large amount
of data in the order of several TBs in a day, the storage of which becomes
challenging. If we are to reduce the effort for the processing units inside the
car, we need to uplink the data to edge or an appropriately placed cloud.
However, the required data rates in the order of several Gbps are difficult to
be met even with the advent of 5G. Therefore, we propose to have a coordinated
set of LiDAR's outside at an elevation which can provide an integrated view
with a much larger field of vision (FoV) to a centralized decision making body
which then sends the required control actions to the vehicles with a lower bit
rate in the downlink and with the required latency. The calculations we have
based on industry standard equipment from several manufacturers show that this
is not just a concept but a feasible system which can be implemented.The
proposed system can play a supportive role with existing autonomous vehicle
architecture and it is easily applicable in an urban area
- …