8 research outputs found
Polygon Intersection-over-Union Loss for Viewpoint-Agnostic Monocular 3D Vehicle Detection
Monocular 3D object detection is a challenging task because depth information
is difficult to obtain from 2D images. A subset of viewpoint-agnostic monocular
3D detection methods also do not explicitly leverage scene homography or
geometry during training, meaning that a model trained thusly can detect
objects in images from arbitrary viewpoints. Such works predict the projections
of the 3D bounding boxes on the image plane to estimate the location of the 3D
boxes, but these projections are not rectangular so the calculation of IoU
between these projected polygons is not straightforward. This work proposes an
efficient, fully differentiable algorithm for the calculation of IoU between
two convex polygons, which can be utilized to compute the IoU between two 3D
bounding box footprints viewed from an arbitrary angle. We test the performance
of the proposed polygon IoU loss (PIoU loss) on three state-of-the-art
viewpoint-agnostic 3D detection models. Experiments demonstrate that the
proposed PIoU loss converges faster than L1 loss and that in 3D detection
models, a combination of PIoU loss and L1 loss gives better results than L1
loss alone (+1.64% AP70 for MonoCon on cars, +0.18% AP70 for RTM3D on cars, and
+0.83%/+2.46% AP50/AP25 for MonoRCNN on cyclists)
The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking
This work presents a novel video dataset recorded from overlapping highway
traffic cameras along an urban interstate, enabling multi-camera 3D object
tracking in a traffic monitoring context. Data is released from 3 scenes
containing video from at least 16 cameras each, totaling 57 minutes in length.
877,000 3D bounding boxes and corresponding object tracklets are fully and
accurately annotated for each camera field of view and are combined into a
spatially and temporally continuous set of vehicle trajectories for each scene.
Lastly, existing algorithms are combined to benchmark a number of 3D
multi-camera tracking pipelines on the dataset, with results indicating that
the dataset is challenging due to the difficulty of matching objects traveling
at high speeds across cameras and heavy object occlusion, potentially for
hundreds of frames, during congested traffic. This work aims to enable the
development of accurate and automatic vehicle trajectory extraction algorithms,
which will play a vital role in understanding impacts of autonomous vehicle
technologies on the safety and efficiency of traffic
So you think you can track?
This work introduces a multi-camera tracking dataset consisting of 234 hours
of video data recorded concurrently from 234 overlapping HD cameras covering a
4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video
is recorded during a period of high traffic density with 500+ objects typically
visible within the scene and typical object longevities of 3-15 minutes. GPS
trajectories from 270 vehicle passes through the scene are manually corrected
in the video data to provide a set of ground-truth trajectories for
recall-oriented tracking metrics, and object detections are provided for each
camera in the scene (159 million total before cross-camera fusion). Initial
benchmarking of tracking-by-detection algorithms is performed against the GPS
trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at
IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked
trackers do not perform sufficiently well at the long temporal and spatial
durations required for traffic scene understanding
Traffic smoothing using explicit local controllers
The dissipation of stop-and-go waves attracted recent attention as a traffic
management problem, which can be efficiently addressed by automated driving. As
part of the 100 automated vehicles experiment named MegaVanderTest, feedback
controls were used to induce strong dissipation via velocity smoothing. More
precisely, a single vehicle driving differently in one of the four lanes of
I-24 in the Nashville area was able to regularize the velocity profile by
reducing oscillations in time and velocity differences among vehicles.
Quantitative measures of this effect were possible due to the innovative I-24
MOTION system capable of monitoring the traffic conditions for all vehicles on
the roadway. This paper presents the control design, the technological aspects
involved in its deployment, and, finally, the results achieved by the
experiment.Comment: 21 pages, 1 Table , 9 figure
Are commercially implemented adaptive cruise control systems string stable?
International audienceIn this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. Seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in car-following experiments with ACC engaged by the follower vehicle. The resulting dataset is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the black box ACC systems. The string stability of the data-fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle model that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle platoon experiment in which all vehicles are the same year, make, and model. In this test, an initial disturbance of 6 mph is amplified to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC. The data collected in the driving experiments is made available, representing the largest publicly available comparative driving dataset on ACC equipped vehicles
Are commercially implemented adaptive cruise control systems string stable?
In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. Seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in car-following experiments with ACC engaged by the follower vehicle. The resulting dataset is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the black box ACC systems. The string stability of the data-fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle model that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle platoon experiment in which all vehicles are the same year, make, and model. In this test, an initial disturbance of 6 mph is amplified to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC. The data collected in the driving experiments is made available, representing the largest publicly available comparative driving dataset on ACC equipped vehicles