50 research outputs found
Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Urban traffic scenarios often require a high degree of cooperation between
traffic participants to ensure safety and efficiency. Observing the behavior of
others, humans infer whether or not others are cooperating. This work aims to
extend the capabilities of automated vehicles, enabling them to cooperate
implicitly in heterogeneous environments. Continuous actions allow for
arbitrary trajectories and hence are applicable to a much wider class of
problems than existing cooperative approaches with discrete action spaces.
Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS)
in conjunction with Decoupled-UCT evaluates the action-values of each agent in
a cooperative and decentralized way, respecting the interdependence of actions
among traffic participants. The extension to continuous action spaces is
addressed by incorporating novel MCTS-specific enhancements for efficient
search space exploration. The proposed algorithm is evaluated under different
scenarios, showing that the algorithm is able to achieve effective cooperative
planning and generate solutions egocentric planning fails to identify
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search
Efficient driving in urban traffic scenarios requires foresight. The
observation of other traffic participants and the inference of their possible
next actions depending on the own action is considered cooperative prediction
and planning. Humans are well equipped with the capability to predict the
actions of multiple interacting traffic participants and plan accordingly,
without the need to directly communicate with others. Prior work has shown that
it is possible to achieve effective cooperative planning without the need for
explicit communication. However, the search space for cooperative plans is so
large that most of the computational budget is spent on exploring the search
space in unpromising regions that are far away from the solution. To accelerate
the planning process, we combined learned heuristics with a cooperative
planning method to guide the search towards regions with promising actions,
yielding better solutions at lower computational costs
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Grid maps are widely used in robotics to represent obstacles in the
environment and differentiating dynamic objects from static infrastructure is
essential for many practical applications. In this work, we present a methods
that uses a deep convolutional neural network (CNN) to infer whether grid cells
are covering a moving object or not. Compared to tracking approaches, that use
e.g. a particle filter to estimate grid cell velocities and then make a
decision for individual grid cells based on this estimate, our approach uses
the entire grid map as input image for a CNN that inspects a larger area around
each cell and thus takes the structural appearance in the grid map into account
to make a decision. Compared to our reference method, our concept yields a
performance increase from 83.9% to 97.2%. A runtime optimized version of our
approach yields similar improvements with an execution time of just 10
milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and
it was accapted at IV 201
Self Supervised Clustering of Traffic Scenes using Graph Representations
Examining graphs for similarity is a well-known challenge, but one that is
mandatory for grouping graphs together. We present a data-driven method to
cluster traffic scenes that is self-supervised, i.e. without manual labelling.
We leverage the semantic scene graph model to create a generic graph embedding
of the traffic scene, which is then mapped to a low-dimensional embedding space
using a Siamese network, in which clustering is performed. In the training
process of our novel approach, we augment existing traffic scenes in the
Cartesian space to generate positive similarity samples. This allows us to
overcome the challenge of reconstructing a graph and at the same time obtain a
representation to describe the similarity of traffic scenes. We could show,
that the resulting clusters possess common semantic characteristics. The
approach was evaluated on the INTERACTION dataset
Anomaly Detection in Autonomous Driving: A Survey
Nowadays, there are outstanding strides towards a future with autonomous
vehicles on our roads. While the perception of autonomous vehicles performs
well under closed-set conditions, they still struggle to handle the unexpected.
This survey provides an extensive overview of anomaly detection techniques
based on camera, lidar, radar, multimodal and abstract object level data. We
provide a systematization including detection approach, corner case level,
ability for an online application, and further attributes. We outline the
state-of-the-art and point out current research gaps.Comment: Daniel Bogdoll and Maximilian Nitsche contributed equally. Accepted
for publication at CVPR 2022 WAD worksho
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence
Increasing the model capacity is a known approach to enhance the adversarial
robustness of deep learning networks. On the other hand, various model
compression techniques, including pruning and quantization, can reduce the size
of the network while preserving its accuracy. Several recent studies have
addressed the relationship between model compression and adversarial
robustness, while some experiments have reported contradictory results. This
work summarizes available evidence and discusses possible explanations for the
observed effects.Comment: Accepted for publication at SSCI 202
One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving
The core obstacle towards a large-scale deployment of autonomous vehicles
currently lies in the long tail of rare events. These are extremely challenging
since they do not occur often in the utilized training data for deep neural
networks. To tackle this problem, we propose the generation of additional
synthetic training data, covering a wide variety of corner case scenarios. As
ontologies can represent human expert knowledge while enabling computational
processing, we use them to describe scenarios. Our proposed master ontology is
capable to model scenarios from all common corner case categories found in the
literature. From this one master ontology, arbitrary scenario-describing
ontologies can be derived. In an automated fashion, these can be converted into
the OpenSCENARIO format and subsequently executed in simulation. This way, also
challenging test and evaluation scenarios can be generated.Comment: Daniel Bogdoll and Stefani Guneshka contributed equally. Accepted for
publication at ECCV 2022 SAIAD worksho
A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving
The verification and validation of autonomous driving vehicles remains a
major challenge due to the high complexity of autonomous driving functions.
Scenario-based testing is a promising method for validating such a complex
system. Ontologies can be utilized to produce test scenarios that are both
meaningful and relevant. One crucial aspect of this process is selecting the
appropriate method for describing the entities involved. The level of detail
and specific entity classes required will vary depending on the system being
tested. It is important to choose an ontology that properly reflects these
needs.
This paper summarizes key representative ontologies for scenario-based
testing and related use cases in the field of autonomous driving. The
considered ontologies are classified according to their level of detail for
both static facts and dynamic aspects. Furthermore, the ontologies are
evaluated based on the presence of important entity classes and the relations
between them