423 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
WiDEVIEW: An UltraWideBand and Vision Dataset for Deciphering Pedestrian-Vehicle Interactions
Robust and accurate tracking and localization of road users like pedestrians
and cyclists is crucial to ensure safe and effective navigation of Autonomous
Vehicles (AVs), particularly so in urban driving scenarios with complex
vehicle-pedestrian interactions. Existing datasets that are useful to
investigate vehicle-pedestrian interactions are mostly image-centric and thus
vulnerable to vision failures. In this paper, we investigate Ultra-wideband
(UWB) as an additional modality for road users' localization to enable a better
understanding of vehicle-pedestrian interactions. We present WiDEVIEW, the
first multimodal dataset that integrates LiDAR, three RGB cameras, GPS/IMU, and
UWB sensors for capturing vehicle-pedestrian interactions in an urban
autonomous driving scenario. Ground truth image annotations are provided in the
form of 2D bounding boxes and the dataset is evaluated on standard 2D object
detection and tracking algorithms. The feasibility of UWB is evaluated for
typical traffic scenarios in both line-of-sight and non-line-of-sight
conditions using LiDAR as ground truth. We establish that UWB range data has
comparable accuracy with LiDAR with an error of 0.19 meters and reliable
anchor-tag range data for up to 40 meters in line-of-sight conditions. UWB
performance for non-line-of-sight conditions is subjective to the nature of the
obstruction (trees vs. buildings). Further, we provide a qualitative analysis
of UWB performance for scenarios susceptible to intermittent vision failures.
The dataset can be downloaded via https://github.com/unmannedlab/UWB_Dataset
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
Pedestrian intention recognition is very important to develop robust and safe
autonomous driving (AD) and advanced driver assistance systems (ADAS)
functionalities for urban driving. In this work, we develop an end-to-end
pedestrian intention framework that performs well on day- and night- time
scenarios. Our framework relies on objection detection bounding boxes combined
with skeletal features of human pose. We study early, late, and combined (early
and late) fusion mechanisms to exploit the skeletal features and reduce false
positives as well to improve the intention prediction performance. The early
fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for
pedestrian intention classification. Furthermore, we propose three new metrics
to properly evaluate the pedestrian intention systems. Under these new
evaluation metrics for the intention prediction, the proposed end-to-end
network offers accurate pedestrian intention up to half a second ahead of the
actual risky maneuver.Comment: 5 pages, 6 figures, 5 tables, IEEE Asilomar SS
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