10 research outputs found
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Modern cars are incorporating an increasing number of driver assist features,
among which automatic lane keeping. The latter allows the car to properly
position itself within the road lanes, which is also crucial for any subsequent
lane departure or trajectory planning decision in fully autonomous cars.
Traditional lane detection methods rely on a combination of highly-specialized,
hand-crafted features and heuristics, usually followed by post-processing
techniques, that are computationally expensive and prone to scalability due to
road scene variations. More recent approaches leverage deep learning models,
trained for pixel-wise lane segmentation, even when no markings are present in
the image due to their big receptive field. Despite their advantages, these
methods are limited to detecting a pre-defined, fixed number of lanes, e.g.
ego-lanes, and can not cope with lane changes. In this paper, we go beyond the
aforementioned limitations and propose to cast the lane detection problem as an
instance segmentation problem - in which each lane forms its own instance -
that can be trained end-to-end. To parametrize the segmented lane instances
before fitting the lane, we further propose to apply a learned perspective
transformation, conditioned on the image, in contrast to a fixed "bird's-eye
view" transformation. By doing so, we ensure a lane fitting which is robust
against road plane changes, unlike existing approaches that rely on a fixed,
pre-defined transformation. In summary, we propose a fast lane detection
algorithm, running at 50 fps, which can handle a variable number of lanes and
cope with lane changes. We verify our method on the tuSimple dataset and
achieve competitive results
RCLane: Relay Chain Prediction for Lane Detection
Lane detection is an important component of many real-world autonomous
systems. Despite a wide variety of lane detection approaches have been
proposed, reporting steady benchmark improvements over time, lane detection
remains a largely unsolved problem. This is because most of the existing lane
detection methods either treat the lane detection as a dense prediction or a
detection task, few of them consider the unique topologies (Y-shape,
Fork-shape, nearly horizontal lane) of the lane markers, which leads to
sub-optimal solution. In this paper, we present a new method for lane detection
based on relay chain prediction. Specifically, our model predicts a
segmentation map to classify the foreground and background region. For each
pixel point in the foreground region, we go through the forward branch and
backward branch to recover the whole lane. Each branch decodes a transfer map
and a distance map to produce the direction moving to the next point, and how
many steps to progressively predict a relay station (next point). As such, our
model is able to capture the keypoints along the lanes. Despite its simplicity,
our strategy allows us to establish new state-of-the-art on four major
benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.Comment: ECCV 202
Aeronautical Engineering: A special bibliography with indexes, supplement 39
This special bibliography lists 417 reports, articles, and other documents introduced into the NASA scientific and technical information system in December 1973
Fifteenth Annual Report of the Bureau of Ethnology to the Secretary of the Smithsonian Institution 1893-\u2794.
15th Annual Report of the Bureau of Ethnology. (no date) HD 339, 54-2, v68, 487p. [3544] Research related to the American Indian; reports on stone implements (Holmes), Siouan Indians (McGee), Siouan sociology (Dorsey), Tusayan Katcinas (Fewkes), and the Casa Grande ruins in Arizona (Mindeleff)