6,420 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
Safety-driven Interactive Planning for Neural Network-based Lane Changing
Neural network-based driving planners have shown great promises in improving
task performance of autonomous driving. However, it is critical and yet very
challenging to ensure the safety of systems with neural network based
components, especially in dense and highly interactive traffic environments. In
this work, we propose a safety-driven interactive planning framework for neural
network-based lane changing. To prevent over conservative planning, we identify
the driving behavior of surrounding vehicles and assess their aggressiveness,
and then adapt the planned trajectory for the ego vehicle accordingly in an
interactive manner. The ego vehicle can proceed to change lanes if a safe
evasion trajectory exists even in the predicted worst case; otherwise, it can
stay around the current lateral position or return back to the original lane.
We quantitatively demonstrate the effectiveness of our planner design and its
advantage over baseline methods through extensive simulations with diverse and
comprehensive experimental settings, as well as in real-world scenarios
collected by an autonomous vehicle company
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Tactical decision making for autonomous driving is challenging due to the
diversity of environments, the uncertainty in the sensor information, and the
complex interaction with other road users. This paper introduces a general
framework for tactical decision making, which combines the concepts of planning
and learning, in the form of Monte Carlo tree search and deep reinforcement
learning. The method is based on the AlphaGo Zero algorithm, which is extended
to a domain with a continuous state space where self-play cannot be used. The
framework is applied to two different highway driving cases in a simulated
environment and it is shown to perform better than a commonly used baseline
method. The strength of combining planning and learning is also illustrated by
a comparison to using the Monte Carlo tree search or the neural network policy
separately
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