1,612 research outputs found
A Unified View of Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critical
applications has motivated research on formal verification of Neural Network
(NN) models. Despite the reputation of learned NN models to behave as black
boxes and the theoretical hardness of proving their properties, researchers
have been successful in verifying some classes of models by exploiting their
piecewise linear structure and taking insights from formal methods such as
Satisifiability Modulo Theory. These methods are however still far from scaling
to realistic neural networks. To facilitate progress on this crucial area, we
make two key contributions. First, we present a unified framework that
encompasses previous methods. This analysis results in the identification of
new methods that combine the strengths of multiple existing approaches,
accomplishing a speedup of two orders of magnitude compared to the previous
state of the art. Second, we propose a new data set of benchmarks which
includes a collection of previously released testcases. We use the benchmark to
provide the first experimental comparison of existing algorithms and identify
the factors impacting the hardness of verification problems.Comment: Updated version of "Piecewise Linear Neural Network verification: A
comparative study
Optimisation-based verification process of obstacle avoidance systems for unmanned vehicles
This thesis deals with safety verification analysis of collision avoidance systems for unmanned vehicles. The safety of the vehicle is dependent on collision avoidance algorithms and associated control laws, and it must be proven that the collision avoidance algorithms and controllers are functioning correctly in all nominal conditions, various failure conditions and in the presence of possible variations in the vehicle and operational environment. The current widely used exhaustive search based approaches are not suitable for safety analysis of autonomous vehicles due to the large number of possible variations and the complexity of algorithms and the systems. To address this topic, a new optimisation-based verification method is developed to verify the safety of collision avoidance systems.
The proposed verification method formulates the worst case analysis problem arising the verification of collision avoidance systems into an optimisation problem and employs optimisation algorithms to automatically search the worst cases. Minimum distance to the obstacle during the collision avoidance manoeuvre is defined as the objective function of the optimisation problem, and realistic simulation consisting of the detailed vehicle dynamics, the operational environment, the collision avoidance algorithm and low level control laws is embedded in the optimisation process. This enables the verification process to take into account the parameters variations in the vehicle, the change of the environment, the uncertainties in sensors, and in particular the mismatching between model used for developing the collision avoidance algorithms and the real vehicle. It is shown that the resultant simulation based optimisation problem is non-convex and there might be many local optima.
To illustrate and investigate the proposed optimisation based verification process, the potential field method and decision making collision avoidance method are chosen as an obstacle avoidance candidate technique for verification study. Five benchmark case studies are investigated in this thesis: static obstacle avoidance system of a simple unicycle robot, moving obstacle avoidance system for a Pioneer 3DX robot, and a 6 Degrees of Freedom fixed wing Unmanned Aerial Vehicle with static and moving collision avoidance algorithms. It is proven that although a local optimisation method for nonlinear optimisation is quite efficient, it is not able to find the most dangerous situation. Results in this thesis show that, among all the global optimisation methods that have been investigated, the DIviding RECTangle method provides most promising performance for verification of collision avoidance functions in terms of guaranteed capability in searching worst scenarios
A Novel Collision Avoidance Logic for Unmanned Aerial Vehicles Using Real-Time Trajectory Planning
An effective collision avoidance logic should prevent collision without excessive
alerting. This requirement would be even more stringent for an
automatic collision avoidance logic, which is probably required by Unmanned
Aerial Vehicles to mitigate the impact of delayed or lost link issues.
In order to improve the safety performance and reduce the frequency
of false alarms, this thesis proposes a novel collision avoidance logic based
on the three-layer architecture and a real-time trajectory planning method.
The aim of this thesis is to develop a real-time trajectory planning algorithm
for the proposed collision avoidance logic and to determine the integrated
logic’s feasibility, merits and limitations for practical applications.
To develop the trajectory planning algorithm, an optimal control problem
is formulated and an inverse-dynamic direct method along with a two
stage, derivative-free pattern search method is used as the solution approach.
The developed algorithm is able to take into account the flyability
of three dimensional manoeuvres, the robustness to the intruder state uncertainty
and the field-of-regard restriction of surveillance sensors. The
testing results show that the standalone executable of the algorithm is able
to provide a flyable avoidance trajectory with a maximum computation
time less than 0.5 seconds.
To evaluate the performance of the proposed logic, an evaluation framework
for Monte Carlo simulations and a baseline approach for comparison
are constructed. Based on five Monte Carlo simulation experiments, it is
found that the proposed logic should be feasible as 1) it is able to achieve
an update rate of 2Hz, 2) its safety performance is comparable with a reference
requirement from another initial feasibility study, and 3) despite a
0.5 seconds computation latency, it outperforms the baseline approach in
terms of safety performance and robustness to sensor and feedback error
Automatic-dependent surveillance-broadcast experimental deployment using system wide information management
This paper describes an automatic-dependent surveillance-broadcast (ADS-B) implementation for air-to-air and ground-based experimental surveillance within a prototype of a fully automated air traffic management (ATM) system, under a trajectory-based-operations paradigm. The system is built using an air-inclusive implementation of system wide information management (SWIM). This work describes the relations between airborne and ground surveillance (SURGND), the prototype surveillance systems, and their algorithms. System's performance is analyzed with simulated and real data. Results show that the proposed ADS-B implementation can fulfill the most demanding surveillance accuracy requirements
Guidelines for assessing pedestrian evacuation software applications
This paper serves to clearly identify and explain criteria to consider when evaluating the
suitability of a pedestrian evacuation software application to assess the evacuation
process of a building. Guidelines in the form of nine topic areas identify different
modelling approaches adopted, as well as features / functionality provided by
applications designed specifically for simulating the egress of pedestrians from inside a
building. The paper concludes with a synopsis of these guidelines, identifying key
questions (by topic area) to found an evaluation
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