433 research outputs found
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Many systems for autonomous vehicles' navigation rely on lane detection.
Traditional algorithms usually estimate only the position of the lanes on the
road, but an autonomous control system may also need to know if a lane marking
can be crossed or not, and what portion of space inside the lane is free from
obstacles, to make safer control decisions. On the other hand, free space
detection algorithms only detect navigable areas, without information about
lanes. State-of-the-art algorithms use CNNs for both tasks, with significant
consumption of computing resources. We propose a novel approach that estimates
the free space inside each lane, with a single CNN. Additionally, adding only a
small requirement concerning GPU RAM, we infer the road type, that will be
useful for path planning. To achieve this result, we train a multi-task CNN.
Then, we further elaborate the output of the network, to extract polygons that
can be effectively used in navigation control. Finally, we provide a
computationally efficient implementation, based on ROS, that can be executed in
real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019
Incremental Adversarial Domain Adaptation for Continually Changing Environments
Continuous appearance shifts such as changes in weather and lighting
conditions can impact the performance of deployed machine learning models.
While unsupervised domain adaptation aims to address this challenge, current
approaches do not utilise the continuity of the occurring shifts. In
particular, many robotics applications exhibit these conditions and thus
facilitate the potential to incrementally adapt a learnt model over minor
shifts which integrate to massive differences over time. Our work presents an
adversarial approach for lifelong, incremental domain adaptation which benefits
from unsupervised alignment to a series of intermediate domains which
successively diverge from the labelled source domain. We empirically
demonstrate that our incremental approach improves handling of large appearance
changes, e.g. day to night, on a traversable-path segmentation task compared
with a direct, single alignment step approach. Furthermore, by approximating
the feature distribution for the source domain with a generative adversarial
network, the deployment module can be rendered fully independent of retaining
potentially large amounts of the related source training data for only a minor
reduction in performance.Comment: International Conference on Robotics and Automation 201
TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China
TiEV is an autonomous driving platform implemented by Tongji University of
China. The vehicle is drive-by-wire and is fully powered by electricity. We
devised the software system of TiEV from scratch, which is capable of driving
the vehicle autonomously in urban paths as well as on fast express roads. We
describe our whole system, especially novel modules of probabilistic perception
fusion, incremental mapping, the 1st and the 2nd planning and the overall
safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future
Challenge of China held at Changshu. We show our experiences on the development
of autonomous vehicles and future trends
Extending the stixel world with online self-supervised color modeling for road-versus-obstacle segmentation
This work concentrates on vision processing for ADAS and intelligent vehicle applications. We propose a color extension to the disparity-based Stixel World method, so that the road can be robustly distinguished from obstacles with respect to erroneous disparity measurements. Our extension learns color appearance models for road and obstacle classes in an online and self-supervised fashion. The algorithm is tightly integrated within the core of the optimization process of the original Stixel World, allowing for strong fusion of the disparity and color signals. We perform an extensive evaluation, including different self-supervised learning strategies and different color models. Our newly recorded, publicly available data set is intentionally focused on challenging traffic scenes with many low-texture regions, causing numerous disparity artifacts. In this evaluation, we increase the F-score of the drivable distance from 0.86 to 0.97, compared to a tuned version of the state-of-the-art baseline method. This clearly shows that our color extension increases the robustness of the Stixel World, by reducing the number of falsely detected obstacles while not deteriorating the detection of true obstacles
TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories
Robustly classifying ground infrastructure such as roads and street crossings
is an essential task for mobile robots operating alongside pedestrians. While
many semantic segmentation datasets are available for autonomous vehicles,
models trained on such datasets exhibit a large domain gap when deployed on
robots operating in pedestrian spaces. Manually annotating images recorded from
pedestrian viewpoints is both expensive and time-consuming. To overcome this
challenge, we propose TrackletMapper, a framework for annotating ground surface
types such as sidewalks, roads, and street crossings from object tracklets
without requiring human-annotated data. To this end, we project the robot
ego-trajectory and the paths of other traffic participants into the ego-view
camera images, creating sparse semantic annotations for multiple types of
ground surfaces from which a ground segmentation model can be trained. We
further show that the model can be self-distilled for additional performance
benefits by aggregating a ground surface map and projecting it into the camera
images, creating a denser set of training annotations compared to the sparse
tracklet annotations. We qualitatively and quantitatively attest our findings
on a novel large-scale dataset for mobile robots operating in pedestrian areas.
Code and dataset will be made available at
http://trackletmapper.cs.uni-freiburg.de.Comment: 19 pages, 14 figures, CoRL 2022 v
- …