580 research outputs found
Continuous Risk Measures for Driving Support
In this paper, we compare three different model-based risk measures by
evaluating their stengths and weaknesses qualitatively and testing them
quantitatively on a set of real longitudinal and intersection scenarios. We
start with the traditional heuristic Time-To-Collision (TTC), which we extend
towards 2D operation and non-crash cases to retrieve the
Time-To-Closest-Encounter (TTCE). The second risk measure models position
uncertainty with a Gaussian distribution and uses spatial occupancy
probabilities for collision risks. We then derive a novel risk measure based on
the statistics of sparse critical events and so-called survival conditions. The
resulting survival analysis shows to have an earlier detection time of crashes
and less false positive detections in near-crash and non-crash cases supported
by its solid theoretical grounding. It can be seen as a generalization of TTCE
and the Gaussian method which is suitable for the validation of ADAS and AD
Introducing Risk Shadowing For Decisive and Comfortable Behavior Planning
We consider the problem of group interactions in urban driving.
State-of-the-art behavior planners for self-driving cars mostly consider each
single agent-to-agent interaction separately in a cost function in order to
find an optimal behavior for the ego agent, such as not colliding with any of
the other agents. In this paper, we develop risk shadowing, a situation
understanding method that allows us to go beyond single interactions by
analyzing group interactions between three agents. Concretely, the presented
method can find out which first other agent does not need to be considered in
the behavior planner of an ego agent, because this first other agent cannot
reach the ego agent due to a second other agent obstructing its way. In
experiments, we show that using risk shadowing as an upstream filter module for
a behavior planner allows to plan more decisive and comfortable driving
strategies than state of the art, given that safety is ensured in these cases.
The usability of the approach is demonstrated for different intersection
scenarios and longitudinal driving.Comment: Accepted at IEEE ITSC 202
Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots
Service robots need common-sense knowledge to help humans in everyday
situations as it enables them to understand the context of their actions.
However, approaches that use ontologies face a challenge because common-sense
knowledge is often implicit, i.e., it is obvious to humans but not explicitly
stated. This paper investigates if Large Language Models (LLMs) can fill this
gap. Our experiments reveal limited effectiveness in the selective extraction
of contextual action knowledge, suggesting that LLMs may not be sufficient on
their own. However, the large-scale extraction of general, actionable knowledge
shows potential, indicating that LLMs can be a suitable tool for efficiently
creating ontologies for robots. This paper shows that the technique used for
knowledge extraction can be applied to populate a minimalist ontology,
showcasing the potential of LLMs in synergy with formal knowledge
representation.Comment: Accepted at ISWC 2023 Posters and Demos: 22nd International Semantic
Web Conference, November 6-10, 2023, Athens, Greec
Proactive Risk Navigation System for Real-World Urban Intersections
We consider the problem of intelligently navigating through complex traffic.
Urban situations are defined by the underlying map structure and special
regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles
(cars, bicycles, etc.) move forward, while trying to keep accident risks low.
Especially at intersections, the combination and interaction of traffic
elements is diverse and human drivers need to focus on specific elements which
are critical for their behavior. To support the analysis, we present in this
paper the so-called Risk Navigation System (RNS). RNS leverages a graph-based
local dynamic map with Time-To-X indicators for extracting upcoming sharp
curves, intersection zones and possible vehicle-to-object collision points.
In real car recordings, recommended velocity profiles to avoid risks are
visualized within a 2D environment. By focusing on communicating not only the
positional but also the temporal relation, RNS potentially helps to enhance
awareness and prediction capabilities of the user
Continuous Risk Measures for Driving Support
In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called “survival” conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD
Dynamic Visual Motion Estimation
Visual motion is the projection of scene movements on a visual sensor. It is a rich source
of information for the analysis of a visual scene. Especially for dynamic vision systems the
estimation of visual motion is important because it allows to deduce the motion of objects as
well as the self-motion of the system relative to the environment. Therefore, visual motion
serves as a basic information for navigation and exploration tasks, like obstacle avoidance,
object tracking or visual scene decomposition into static and moving parts
Learning viewpoint invariant object representations using a temporal coherence principle
Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability” objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an - also unlabeled - distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformation
Comfortable Priority Handling with Predictive Velocity Optimization for Intersection Crossings
We address the problem of motion planning for four-way intersection crossings
with right-of-ways. Road safety typically assigns liability to the follower in
rear-end collisions and to the approaching vehicle required to yield in side
crashes. As an alternative to previous models based on heuristic state
machines, we propose a planning framework which changes the prediction model of
other cars (e.g. their prototypical accelerations and decelerations) depending
on the given longitudinal or lateral priority rules. Combined with a
state-of-the-art trajectory optimization approach ROPT (Risk Optimization
Method) this allows to find ego velocity profiles minimizing risks from curves
and all involved vehicles while maximizing utility (needed time to arrive at a
goal) and comfort (change and duration of acceleration) under the presence of
regulatory conditions. Analytical and statistical evaluations show that our
method is able to follow right-of-ways for a wide range of other vehicle
behaviors and path geometries. Even when the other cars drive in a
non-priority-compliant way, ROPT achieves good risk-comfort tradeoffs
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