6,482 research outputs found
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 Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
Trajectory generation for road vehicle obstacle avoidance using convex optimization
This paper presents a method for trajectory generation using convex optimization to find a feasible, obstacle-free path for a road vehicle. Consideration of vehicle rotation is shown to be necessary if the trajectory is to avoid obstacles specified in a fixed Earth axis system. The paper establishes that, despite the presence of significant non-linearities, it is possible to articulate the obstacle avoidance problem in a tractable convex form using multiple optimization passes. Finally, it is shown by simulation that an optimal trajectory that accounts for the vehicle’s changing velocity throughout the manoeuvre is superior to a previous analytical method that assumes constant speed
Probabilistic Threat Assessment and Driver Modeling in Collision Avoidance Systems
This paper presents a probabilistic framework for decision-making in collision avoidance systems, targeting all types of collision scenarios with all types of single road users and objects. Decisions on when and how to assist the driver are made by taking a Bayesian approach to estimate how a collision can be avoided by an autonomous brake intervention, and the probability that the driver will consider the intervention as motivated. The driver model makes it possible to initiate earlier braking when it is estimated that the driver acceptance for interventions is high. The framework and the proposed driver model are evaluated in several scenarios, using authentic tracker data and a differential GPS. It is shown that the driver model can increase the benefit of collision avoidance systems — particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict, e.g. when a playing child enters the roadway
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