20,675 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
AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
We present AutonoVi:, a novel algorithm for autonomous vehicle navigation
that supports dynamic maneuvers and satisfies traffic constraints and norms.
Our approach is based on optimization-based maneuver planning that supports
dynamic lane-changes, swerving, and braking in all traffic scenarios and guides
the vehicle to its goal position. We take into account various traffic
constraints, including collision avoidance with other vehicles, pedestrians,
and cyclists using control velocity obstacles. We use a data-driven approach to
model the vehicle dynamics for control and collision avoidance. Furthermore,
our trajectory computation algorithm takes into account traffic rules and
behaviors, such as stopping at intersections and stoplights, based on an
arc-spline representation. We have evaluated our algorithm in a simulated
environment and tested its interactive performance in urban and highway driving
scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios
include jaywalking pedestrians, sudden stops from high speeds, safely passing
cyclists, a vehicle suddenly swerving into the roadway, and high-density
traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure
End-to-end Driving via Conditional Imitation Learning
Deep networks trained on demonstrations of human driving have learned to
follow roads and avoid obstacles. However, driving policies trained via
imitation learning cannot be controlled at test time. A vehicle trained
end-to-end to imitate an expert cannot be guided to take a specific turn at an
upcoming intersection. This limits the utility of such systems. We propose to
condition imitation learning on high-level command input. At test time, the
learned driving policy functions as a chauffeur that handles sensorimotor
coordination but continues to respond to navigational commands. We evaluate
different architectures for conditional imitation learning in vision-based
driving. We conduct experiments in realistic three-dimensional simulations of
urban driving and on a 1/5 scale robotic truck that is trained to drive in a
residential area. Both systems drive based on visual input yet remain
responsive to high-level navigational commands. The supplementary video can be
viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation
(ICRA), 201
Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing
This paper applies some previously studied extended
Kalman filter techniques for planar road geometry estimation
to the domain of autonomous navigation of off-highway
vehicles. In this work, a clothoid model of the road geometry is
constructed and estimated recursively based on road features
extracted from single-axis LADAR range measurements. We
present a method for feature extraction of the road centerline
in the image plane, and describe its application to recursive
estimation of the road geometry. We analyze the performance of
our method against simulated motion of varied road geometries
and against closed-loop detection, tracking and following of
desert roads. Our method accomodates full 6 DOF motion of
the vehicle as it navigates, constructs consistent estimates of the
road geometry with respect to a fixed global reference frame,
and requires an estimate of the sensor pose for each range
measurement
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Driving requires reacting to a wide variety of complex environment conditions
and agent behaviors. Explicitly modeling each possible scenario is unrealistic.
In contrast, imitation learning can, in theory, leverage data from large fleets
of human-driven cars. Behavior cloning in particular has been successfully used
to learn simple visuomotor policies end-to-end, but scaling to the full
spectrum of driving behaviors remains an unsolved problem. In this paper, we
propose a new benchmark to experimentally investigate the scalability and
limitations of behavior cloning. We show that behavior cloning leads to
state-of-the-art results, including in unseen environments, executing complex
lateral and longitudinal maneuvers without these reactions being explicitly
programmed. However, we confirm well-known limitations (due to dataset bias and
overfitting), new generalization issues (due to dynamic objects and the lack of
a causal model), and training instability requiring further research before
behavior cloning can graduate to real-world driving. The code of the studied
behavior cloning approaches can be found at
https://github.com/felipecode/coiltraine
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