Automated Scenario Generation Using Halton Sequences for the Verification of Autonomous Vehicle Behavior in Simulation

As autonomous vehicles continue to develop, verifying their safety remains a large hurdle to mass adoption. One component of this is testing, however it has been shown that it is impractical to statistically prove an autonomous vehicle’s safety using real-world testing alone. Therefore, simulation tools and other virtual testing methods are being employed to assist with the verification process. Testing in simulation still faces some of the challenges of the real world, such as the difficulty in exhaustively testing the system in all scenarios it will encounter. Manual scenario creation is time consuming and does not guarantee scenario coverage. Pseudo-random scenario generation is a faster option, but still does not ensure coverage of the state space. Therefore, this study proposes the use of Halton sequences to automatically generate scenarios for autonomous vehicle testing in simulation. It compares these scenarios against a set of pseudo-randomly generated scenarios and assesses the performance of each method to cover the simulation state space and provide an accurate depiction of the capabilities of the system-under-test. These tests are carried out in the CARLA simulation environment on an open source, published driving model called “Learning by Cheating” which takes place as the system-under-test. This study concludes that the scenario set generated by the Halton sequence is better at providing an accurate representation of the capabilities of the system-under-test than the pseudo-random scenario generation method

An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification

Simulation-based verification is beneficial for assessing otherwise dangerous or costly on-road testing of autonomous vehicles (AV). This paper addresses the challenge of efficiently generating effective tests for simulation-based AV verification using software testing agents. The multi-agent system (MAS) programming paradigm offers rational agency, causality and strategic planning between multiple agents. We exploit these aspects for test generation, focusing in particular on the generation of tests that trigger the precondition of an assertion. On the example of a key assertion we show that, by encoding a variety of different behaviours respondent to the agent's perceptions of the test environment, the agency-directed approach generates twice as many effective tests than pseudo-random test generation, while being both efficient and robust. Moreover, agents can be encoded to behave naturally without compromising the effectiveness of test generation. Our results suggest that generating tests using agency-directed testing significantly improves upon random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure

An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification

Simulation-based verification is beneficial for assessing otherwise dangerous or costly on-road testing of autonomous vehicles (AV). This paper addresses the challenge of efficiently generating effective tests for simulation-based AV verification using software testing agents. The multi-agent system (MAS) programming paradigm offers rational agency, causality and strategic planning between multiple agents. We exploit these aspects for test generation, focusing in particular on the generation of tests that trigger the precondition of an assertion. On the example of a key assertion we show that, by encoding a variety of different behaviours respondent to the agent's perceptions of the test environment, the agency-directed approach generates twice as many effective tests than pseudo-random test generation, while being both efficient and robust. Moreover, agents can be encoded to behave naturally without compromising the effectiveness of test generation. Our results suggest that generating tests using agency-directed testing significantly improves upon random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure

Improving RRT for Automated Parking in Real-world Scenarios

Automated parking is a self-driving feature that has been in cars for several years. Parking assistants in currently sold cars fail to park in more complex real-world scenarios and require the driver to move the car to an expected starting position before the assistant is activated. We overcome these limitations by proposing a planning algorithm consisting of two stages: (1) a geometric planner for maneuvering inside the parking slot and (2) a Rapidly-exploring Random Trees (RRT)-based planner that finds a collision-free path from the initial position to the slot entry. Evaluation of computational experiments demonstrates that improvements over commonly used RRT extensions reduce the parking path cost by 21 % and reduce the computation time by 79.5 %. The suitability of the algorithm for real-world parking scenarios was verified in physical experiments with Porsche Cayenne.Comment: 19 pages, 14 figures, 2 table

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Search-based Test Generation for Automated Driving Systems: From Perception to Control Logic

abstract: Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving. One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario. The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Trajectory Planning on Grids: Considering Speed Limit Constraints

Trajectory (path) planning is a well known and thoroughly studied field of automated planning. It is usually used in computer games, robotics or autonomous agent simulations. Grids are often used for regular discretization of continuous space. Many methods exist for trajectory (path) planning on grids, we address the well known A* algorithm and the state-of-the-art Theta* algorithm. Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic. In this paper, we provide an extension of both these algorithms to enable support for speed limit constraints.We experimentally evaluate and thoroughly discuss how the extensions affect the planning process showing reasonability and justification of our approach