332 research outputs found
From Specifications to Behavior: Maneuver Verification in a Semantic State Space
To realize a market entry of autonomous vehicles in the foreseeable future,
the behavior planning system will need to abide by the same rules that humans
follow. Product liability cannot be enforced without a proper solution to the
approval trap. In this paper, we define a semantic abstraction of the
continuous space and formalize traffic rules in linear temporal logic (LTL).
Sequences in the semantic state space represent maneuvers a high-level planner
could choose to execute. We check these maneuvers against the formalized
traffic rules using runtime verification. By using the standard model checker
NuSMV, we demonstrate the effectiveness of our approach and provide runtime
properties for the maneuver verification. We show that high-level behavior can
be verified in a semantic state space to fulfill a set of formalized rules,
which could serve as a step towards safety of the intended functionality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 201
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments
Most reinforcement learning approaches used in behavior generation utilize
vectorial information as input. However, this requires the network to have a
pre-defined input-size -- in semantic environments this means assuming the
maximum number of vehicles. Additionally, this vectorial representation is not
invariant to the order and number of vehicles. To mitigate the above-stated
disadvantages, we propose combining graph neural networks with actor-critic
reinforcement learning. As graph neural networks apply the same network to
every vehicle and aggregate incoming edge information, they are invariant to
the number and order of vehicles. This makes them ideal candidates to be used
as networks in semantic environments -- environments consisting of objects
lists. Graph neural networks exhibit some other advantages that make them
favorable to be used in semantic environments. The relational information is
explicitly given and does not have to be inferred. Moreover, graph neural
networks propagate information through the network and can gather higher-degree
information. We demonstrate our approach using a highway lane-change scenario
and compare the performance of graph neural networks to conventional ones. We
show that graph neural networks are capable of handling scenarios with a
varying number and order of vehicles during training and application
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