7,139 research outputs found
Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
Due to the current developments towards autonomous driving and vehicle active
safety, there is an increasing necessity for algorithms that are able to
perform complex criticality predictions in real-time. Being able to process
multi-object traffic scenarios aids the implementation of a variety of
automotive applications such as driver assistance systems for collision
prevention and mitigation as well as fall-back systems for autonomous vehicles.
We present a fully model-based algorithm with a parallelizable architecture.
The proposed algorithm can evaluate the criticality of complex, multi-modal
(vehicles and pedestrians) traffic scenarios by simulating millions of
trajectory combinations and detecting collisions between objects. The algorithm
is able to estimate upcoming criticality at very early stages, demonstrating
its potential for vehicle safety-systems and autonomous driving applications.
An implementation on an embedded system in a test vehicle proves in a
prototypical manner the compatibility of the algorithm with the hardware
possibilities of modern cars. For a complex traffic scenario with 11 dynamic
objects, more than 86 million pose combinations are evaluated in 21 ms on the
GPU of a Drive PX~2
A New Approach To Estimate The Collision Probability For Automotive Applications
We revisit the computation of probability of collision in the context of
automotive collision avoidance (the estimation of a potential collision is also
referred to as conflict detection in other contexts). After reviewing existing
approaches to the definition and computation of a collision probability we
argue that the question "What is the probability of collision within the next
three seconds?" can be answered on the basis of a collision probability rate.
Using results on level crossings for vector stochastic processes we derive a
general expression for the upper bound of the distribution of the collision
probability rate. This expression is valid for arbitrary prediction models
including process noise. We demonstrate in several examples that distributions
obtained by large-scale Monte-Carlo simulations obey this bound and in many
cases approximately saturate the bound. We derive an approximation for the
distribution of the collision probability rate that can be computed on an
embedded platform. In order to efficiently sample this probability rate
distribution for determination of its characteristic shape an adaptive method
to obtain the sampling points is proposed. An upper bound of the probability of
collision is then obtained by one-dimensional numerical integration over the
time period of interest. A straightforward application of this method applies
to the collision of an extended object with a second point-like object. Using
an abstraction of the second object by salient points of its boundary we
propose an application of this method to two extended objects with arbitrary
orientation. Finally, the distribution of the collision probability rate is
identified as the distribution of the time-to-collision.Comment: Revised and restructured version, discussion of extended vehicles
expanded, section on TTC expanded, references added, other minor changes, 17
pages, 18 figure
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
On the Robotic Uncertainty of Fully Autonomous Traffic
Recent transportation research suggests that autonomous vehicles (AVs) have
the potential to improve traffic flow efficiency as they are able to maintain
smaller car-following distances. Nevertheless, being a unique class of ground
robots, AVs are susceptible to robotic errors, particularly in their perception
module, leading to uncertainties in their movements and an increased risk of
collisions. Consequently, conservative operational strategies, such as larger
headway and slower speeds, are implemented to prioritize safety over traffic
capacity in real-world operations. To reconcile the inconsistency, this paper
proposes an analytical model framework that delineates the endogenous
reciprocity between traffic safety and efficiency that arises from robotic
uncertainty in AVs. Car-following scenarios are extensively examined, with
uncertain headway as the key parameter for bridging the single-lane capacity
and the collision probability. A Markov chain is then introduced to describe
the dynamics of the lane capacity, and the resulting expected
collision-inclusive capacity is adopted as the ultimate performance measure for
fully autonomous traffic. With the help of this analytical model, it is
possible to support the settings of critical parameters in AV operations and
incorporate optimization techniques to assist traffic management strategies for
autonomous traffic
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling
Long-term situation prediction plays a crucial role in the development of
intelligent vehicles. A major challenge still to overcome is the prediction of
complex downtown scenarios with multiple road users, e.g., pedestrians, bikes,
and motor vehicles, interacting with each other. This contribution tackles this
challenge by combining a Bayesian filtering technique for environment
representation, and machine learning as long-term predictor. More specifically,
a dynamic occupancy grid map is utilized as input to a deep convolutional
neural network. This yields the advantage of using spatially distributed
velocity estimates from a single time step for prediction, rather than a raw
data sequence, alleviating common problems dealing with input time series of
multiple sensors. Furthermore, convolutional neural networks have the inherent
characteristic of using context information, enabling the implicit modeling of
road user interaction. Pixel-wise balancing is applied in the loss function
counteracting the extreme imbalance between static and dynamic cells. One of
the major advantages is the unsupervised learning character due to fully
automatic label generation. The presented algorithm is trained and evaluated on
multiple hours of recorded sensor data and compared to Monte-Carlo simulation
Motion Planning for Autonomous Vehicles in Partially Observable Environments
Unsicherheiten, welche aus Sensorrauschen oder nicht beobachtbaren Manöverintentionen anderer Verkehrsteilnehmer resultieren, akkumulieren sich in der Datenverarbeitungskette eines autonomen Fahrzeugs und führen zu einer unvollständigen oder fehlinterpretierten Umfeldrepräsentation. Dadurch weisen Bewegungsplaner in vielen Fällen ein konservatives Verhalten auf.
Diese Dissertation entwickelt zwei Bewegungsplaner, welche die Defizite der vorgelagerten Verarbeitungsmodule durch Ausnutzung der Reaktionsfähigkeit des Fahrzeugs kompensieren. Diese Arbeit präsentiert zuerst eine ausgiebige Analyse über die Ursachen und Klassifikation der Unsicherheiten und zeigt die Eigenschaften eines idealen Bewegungsplaners auf. Anschließend befasst sie sich mit der mathematischen Modellierung der Fahrziele sowie den Randbedingungen, welche die Sicherheit gewährleisten. Das resultierende Planungsproblem wird mit zwei unterschiedlichen Methoden in Echtzeit gelöst: Zuerst mit nichtlinearer Optimierung und danach, indem es als teilweise beobachtbarer Markov-Entscheidungsprozess (POMDP) formuliert und die Lösung mit Stichproben angenähert wird. Der auf nichtlinearer Optimierung basierende Planer betrachtet mehrere Manöveroptionen mit individuellen Auftrittswahrscheinlichkeiten und berechnet daraus ein Bewegungsprofil. Er garantiert Sicherheit, indem er die Realisierbarkeit einer zufallsbeschränkten Rückfalloption gewährleistet. Der Beitrag zum POMDP-Framework konzentriert sich auf die Verbesserung der Stichprobeneffizienz in der Monte-Carlo-Planung. Erstens werden Informationsbelohnungen definiert, welche die Stichproben zu Aktionen führen, die eine höhere Belohnung ergeben. Dabei wird die Auswahl der Stichproben für das reward-shaped Problem durch die Verwendung einer allgemeinen Heuristik verbessert. Zweitens wird die Kontinuität in der Reward-Struktur für die Aktionsauswahl ausgenutzt und dadurch signifikante Leistungsverbesserungen erzielt. Evaluierungen zeigen, dass mit diesen Planern große Erfolge in Fahrversuchen und Simulationsstudien mit komplexen Interaktionsmodellen erreicht werden
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
Fe(3): An Evaluation Tool for Low-Altitude Air Traffic Operations
The concepts of unmanned aircraft system traffic management (UTM) and urban air mobility (UAM) are introducing high-density operations in low altitude airspace in closer proximity to populated areas than conventional high-altitude air traffic. The Flexible engine for Fast-time Evaluation of Flight Environments (Fe (sup 3)) provides the capability of statistically analyzing the high-density, high-fidelity, and low-altitude traffic system under numerous scenarios, such that stake holders can study impacts of factors in the low-altitude high-density traffic system and define requirements, policies, and protocols needed to support a safe yet efficient traffic system, and even assess operational risks and optimize flight schedules without conducting infeasible and cost-prohibitive flight tests that involve a large volume of aerial vehicles. This work provides an introduction to this simulation tool including its architecture and various models involved. Its performance and sample application in UAM and UTM are also presented
- …