1,839 research outputs found
Towards a Complete Safety Framework for Longitudinal Driving
Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework allows calculating minimum safe inter-vehicular distances for arbitrary ego vehicle control policies. We use this framework to enhance the Responsibility-Sensitive Safety (RSS) model and models based on it, which fail to cover situations where the ego vehicle has a higher decelerating capacity than its preceding vehicle. For arbitrary ego vehicle control policies, we show how our framework can be applied by substituting real (possibly computationally intractable) controllers with upper bounding functions. This comprises a general approach for longitudinal safety, where safety guarantees for the upper-bounded system are equivalent to those for the original system but come at the expense of larger inter-vehicular distances
Tackling Occlusions & Limited Sensor Range with Set-based Safety Verification
Provable safety is one of the most critical challenges in automated driving.
The behavior of numerous traffic participants in a scene cannot be predicted
reliably due to complex interdependencies and the indiscriminate behavior of
humans. Additionally, we face high uncertainties and only incomplete
environment knowledge. Recent approaches minimize risk with probabilistic and
machine learning methods - even under occlusions. These generate comfortable
behavior with good traffic flow, but cannot guarantee safety of their
maneuvers.
Therefore, we contribute a safety verification method for trajectories under
occlusions. The field-of-view of the ego vehicle and a map are used to identify
critical sensing field edges, each representing a potentially hidden obstacle.
The state of occluded obstacles is unknown, but can be over-approximated by
intervals over all possible states.
Then set-based methods are extended to provide occupancy predictions for
obstacles with state intervals. The proposed method can verify the safety of
given trajectories (e.g. if they ensure collision-free fail-safe maneuver
options) w.r.t. arbitrary safe-state formulations. The potential for provably
safe trajectory planning is shown in three evaluative scenarios
Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art
The large-scale deployment of automated vehicles on public roads has the
potential to vastly change the transportation modalities of today's society.
Although this pursuit has been initiated decades ago, there still exist open
challenges in reliably ensuring that such vehicles operate safely in open
contexts. While functional safety is a well-established concept, the question
of measuring the behavioral safety of a vehicle remains subject to research.
One way to both objectively and computationally analyze traffic conflicts is
the development and utilization of so-called criticality metrics. Contemporary
approaches have leveraged the potential of criticality metrics in various
applications related to automated driving, e.g. for computationally assessing
the dynamic risk or filtering large data sets to build scenario catalogs. As a
prerequisite to systematically choose adequate criticality metrics for such
applications, we extensively review the state of the art of criticality
metrics, their properties, and their applications in the context of automated
driving. Based on this review, we propose a suitability analysis as a
methodical tool to be used by practitioners. Both the proposed method and the
state of the art review can then be harnessed to select well-suited measurement
tools that cover an application's requirements, as demonstrated by an exemplary
execution of the analysis. Ultimately, efficient, valid, and reliable
measurements of an automated vehicle's safety performance are a key requirement
for demonstrating its trustworthiness
Decision-theoretic MPC: Motion Planning with Weighted Maneuver Preferences Under Uncertainty
Continuous optimization based motion planners require deciding on a maneuver
homotopy before optimizing the trajectory. Under uncertainty, maneuver
intentions of other participants can be unclear, and the vehicle might not be
able to decide on the most suitable maneuver. This work introduces a method
that incorporates multiple maneuver preferences in planning. It optimizes the
trajectory by considering weighted maneuver preferences together with
uncertainties ranging from perception to prediction while ensuring the
feasibility of a chance-constrained fallback option. Evaluations in both
driving experiments and simulation studies show enhanced interaction
capabilities and comfort levels compared to conventional planners, which
consider only a single maneuver
Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches
The growing advancements in Autonomous Vehicles (AVs) have emphasized the
critical need to prioritize the absolute safety of AV maneuvers, especially in
dynamic and unpredictable environments or situations. This objective becomes
even more challenging due to the uniqueness of every traffic
situation/condition. To cope with all these very constrained and complex
configurations, AVs must have appropriate control architectures with reliable
and real-time Risk Assessment and Management Strategies (RAMS). These targeted
RAMS must lead to reduce drastically the navigation risks. However, the lack of
safety guarantees proves, which is one of the key challenges to be addressed,
limit drastically the ambition to introduce more broadly AVs on our roads and
restrict the use of AVs to very limited use cases. Therefore, the focus and the
ambition of this paper is to survey research on autonomous vehicles while
focusing on the important topic of safety guarantee of AVs. For this purpose,
it is proposed to review research on relevant methods and concepts defining an
overall control architecture for AVs, with an emphasis on the safety assessment
and decision-making systems composing these architectures. Moreover, it is
intended through this reviewing process to highlight researches that use either
model-based methods or AI-based approaches. This is performed while emphasizing
the strengths and weaknesses of each methodology and investigating the research
that proposes a comprehensive multi-modal design that combines model-based and
AI approaches. This paper ends with discussions on the methods used to
guarantee the safety of AVs namely: safety verification techniques and the
standardization/generalization of safety frameworks
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