15,534 research outputs found
Imitation Learning for Vision-based Lane Keeping Assistance
This paper aims to investigate direct imitation learning from human drivers
for the task of lane keeping assistance in highway and country roads using
grayscale images from a single front view camera. The employed method utilizes
convolutional neural networks (CNN) to act as a policy that is driving a
vehicle. The policy is successfully learned via imitation learning using
real-world data collected from human drivers and is evaluated in closed-loop
simulated environments, demonstrating good driving behaviour and a robustness
for domain changes. Evaluation is based on two proposed performance metrics
measuring how well the vehicle is positioned in a lane and the smoothness of
the driven trajectory.Comment: International Conference on Intelligent Transportation Systems (ITSC
An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
Due to the complexity of the natural world, a programmer cannot foresee all
possible situations, a connected and autonomous vehicle (CAV) will face during
its operation, and hence, CAVs will need to learn to make decisions
autonomously. Due to the sensing of its surroundings and information exchanged
with other vehicles and road infrastructure, a CAV will have access to large
amounts of useful data. While different control algorithms have been proposed
for CAVs, the benefits brought about by connectedness of autonomous vehicles to
other vehicles and to the infrastructure, and its implications on policy
learning has not been investigated in literature. This paper investigates a
data driven driving policy learning framework through an agent-based modelling
approaches. The contributions of the paper are two-fold. A dynamic programming
framework is proposed for in-vehicle policy learning with and without
connectivity to neighboring vehicles. The simulation results indicate that
while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V)
communication of information improves this capability. Furthermore, to overcome
the limitations of sensing in a CAV, the paper proposes a novel concept for
infrastructure-led policy learning and communication with autonomous vehicles.
In infrastructure-led policy learning, road-side infrastructure senses and
captures successful vehicle maneuvers and learns an optimal policy from those
temporal sequences, and when a vehicle approaches the road-side unit, the
policy is communicated to the CAV. Deep-imitation learning methodology is
proposed to develop such an infrastructure-led policy learning framework
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
We consider the paradigm of a black box AI system that makes life-critical
decisions. We propose an "arguing machines" framework that pairs the primary AI
system with a secondary one that is independently trained to perform the same
task. We show that disagreement between the two systems, without any knowledge
of underlying system design or operation, is sufficient to arbitrarily improve
the accuracy of the overall decision pipeline given human supervision over
disagreements. We demonstrate this system in two applications: (1) an
illustrative example of image classification and (2) on large-scale real-world
semi-autonomous driving data. For the first application, we apply this
framework to image classification achieving a reduction from 8.0% to 2.8% top-5
error on ImageNet. For the second application, we apply this framework to Tesla
Autopilot and demonstrate the ability to predict 90.4% of system disengagements
that were labeled by human annotators as challenging and needing human
supervision
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
To safely and efficiently navigate through complex traffic scenarios,
autonomous vehicles need to have the ability to predict the future motion of
surrounding vehicles. Multiple interacting agents, the multi-modal nature of
driver behavior, and the inherent uncertainty involved in the task make motion
prediction of surrounding vehicles a challenging problem. In this paper, we
present an LSTM model for interaction aware motion prediction of surrounding
vehicles on freeways. Our model assigns confidence values to maneuvers being
performed by vehicles and outputs a multi-modal distribution over future motion
based on them. We compare our approach with the prior art for vehicle motion
prediction on the publicly available NGSIM US-101 and I-80 datasets. Our
results show an improvement in terms of RMS values of prediction error. We also
present an ablative analysis of the components of our proposed model and
analyze the predictions made by the model in complex traffic scenarios.Comment: accepted for publication at IV 201
Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems
This position paper introduces the concept of artificial
“co-drivers” as an enabling technology for future intelligent
transportation systems. In Sections I and II, the design
principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and
the emulation theory of cognition. In Sections III and IV, we
present the co-driver developed for the EU project interactIVe
as an example instantiation of this notion, demonstrating how
it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research
ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
Large-scale driving datasets such as Waymo Open Dataset and nuScenes
substantially accelerate autonomous driving research, especially for perception
tasks such as 3D detection and trajectory forecasting. Since the driving logs
in these datasets contain HD maps and detailed object annotations which
accurately reflect the real-world complexity of traffic behaviors, we can
harvest a massive number of complex traffic scenarios and recreate their
digital twins in simulation. Compared to the hand-crafted scenarios often used
in existing simulators, data-driven scenarios collected from the real world can
facilitate many research opportunities in machine learning and autonomous
driving. In this work, we present ScenarioNet, an open-source platform for
large-scale traffic scenario modeling and simulation. ScenarioNet defines a
unified scenario description format and collects a large-scale repository of
real-world traffic scenarios from the heterogeneous data in various driving
datasets including Waymo, nuScenes, Lyft L5, and nuPlan datasets. These
scenarios can be further replayed and interacted with in multiple views from
Bird-Eye-View layout to realistic 3D rendering in MetaDrive simulator. This
provides a benchmark for evaluating the safety of autonomous driving stacks in
simulation before their real-world deployment. We further demonstrate the
strengths of ScenarioNet on large-scale scenario generation, imitation
learning, and reinforcement learning in both single-agent and multi-agent
settings. Code, demo videos, and website are available at
https://metadriverse.github.io/scenarionet
- …