9 research outputs found
Continual Driving Policy Optimization with Closed-Loop Individualized Curricula
The safety of autonomous vehicles (AV) has been a long-standing top concern,
stemming from the absence of rare and safety-critical scenarios in the
long-tail naturalistic driving distribution. To tackle this challenge, a surge
of research in scenario-based autonomous driving has emerged, with a focus on
generating high-risk driving scenarios and applying them to conduct
safety-critical testing of AV models. However, limited work has been explored
on the reuse of these extensive scenarios to iteratively improve AV models.
Moreover, it remains intractable and challenging to filter through gigantic
scenario libraries collected from other AV models with distinct behaviors,
attempting to extract transferable information for current AV improvement.
Therefore, we develop a continual driving policy optimization framework
featuring Closed-Loop Individualized Curricula (CLIC), which we factorize into
a set of standardized sub-modules for flexible implementation choices: AV
Evaluation, Scenario Selection, and AV Training. CLIC frames AV Evaluation as a
collision prediction task, where it estimates the chance of AV failures in
these scenarios at each iteration. Subsequently, by re-sampling from historical
scenarios based on these failure probabilities, CLIC tailors individualized
curricula for downstream training, aligning them with the evaluated capability
of AV. Accordingly, CLIC not only maximizes the utilization of the vast
pre-collected scenario library for closed-loop driving policy optimization but
also facilitates AV improvement by individualizing its training with more
challenging cases out of those poorly organized scenarios. Experimental results
clearly indicate that CLIC surpasses other curriculum-based training
strategies, showing substantial improvement in managing risky scenarios, while
still maintaining proficiency in handling simpler cases
SCALABLE AND PRACTICAL AUTOMATED TESTING OF DEEP LEARNING MODELS AND SYSTEMS
With the recent advances of Deep Neural Networks (DNNs) in real-world applications, such as Automated Driving Systems (ADS) for self-driving cars, ensuring the reliability and safety of such DNN-Enabled Systems (DES) emerges as a fundamental topic in software testing. Automatically generating new and diverse test data that lead to safety violations of DES presents the following challenges: (1) there can be many safety requirements to be considered at the same time, (2) running a high-fidelity simulator is often very computationally intensive, (3) the space of all possible test data that may trigger safety violations is too large to be exhaustively explored, (4) depending upon the accuracy of the DES under test, it may be infeasible to find a scenario causing violations for some requirements, and (5) DNNs are often developed by a third party, who does not provide access to internal information of the DNNs.
In this dissertation, in collaboration with IEE sensing, we address the aforementioned challenges by providing scalable and practical automated solutions for testing Deep Learning (DL) models and systems. Specifically, we present the following in the dissertation.
1. We conduct an empirical study to compare offline testing and online testing in the context
of Automated Driving Systems (ADS). We also investigate whether simulator-generated data can be used in lieu of real-world data. Furthermore, we investigate whether offline testing results can be used to help reduce the cost of online testing.
2. We propose an approach to generate test data using many-objective search algorithms tailored for test suite generation to generate test data for DNN with many outputs. We also demonstrate a way to learn conditions that cause the DNN to mispredict the outputs.
3. In order to reduce the number of computationally expensive simulations, we propose an automated approach, SAMOTA, to generate data for DNN-enabled automated driving systems, using many- objective search and surrogate-assisted optimisation.
4. The environmental conditions (e.g., weather, lighting) often stay the same during a simulation, which can limit the scope of testing. To address this limitation, we present an automated approach, MORLAT, to dynamically interact with the environment during simulation. MORLAT relies on reinforcement learning and many-objective optimisation.
We evaluate our approaches using state-of-the-art deep neural networks and systems. The results show that our approaches perform statistically better than the alternative
Non-Line of Sight Test Scenario Generation for Connected Autonomous Vehicle
Connected autonomous vehicles (CAV) level 4-5 use sensors to perceive their environment. These sensors are able to detect only up to a certain range and this range can be further constrained by the presence of obstacles in its path or as a result of the geometry of the road, for example, at a junction. This is termed as a non-line of sight (NLOS) scenario where the ego vehicle (system under test) is unable to detect an oncoming dynamic object due to obstacles or the geometry of the road.
A large body of work now exist which proposes methods for extending the perception horizon of CAVâs using vehicular communication and incorporating this into CAV algorithms ranging from obstacle detection to path planning and beyond. Such proposed new algorithms and entire systems needs testing and validating, which can be conducted through primarily two ways, on road testing and simulation. On-road testing can be extremely expensive and time-consuming and may not cover all possible test scenarios. Testing through simulation is inexpensive and has a better scenario space coverage. However, there is currently a dearth in simulated testing techniques that provides the environment to test technologies and algorithms developed for NLOS scenarios.
This thesis puts forward a novel end-to-end framework for testing the abilities of a CAV through simulated generation of NLOS scenarios. This has been achieved through following the development process of Functional, Logical and Concrete scenarios along the V-model-based development process in ISO 26262. The process begins with the representation of the NLOS environment (including the digital environment) knowledge as a scalable ontology where Functional and Logical scenarios stand for different abstraction levels. The proposed new ontology comprises of six layers: âEnvironmentâ, âRoad Userâ, âObject Typeâ, âCommunication Networkâ, âSceneâ and âScenarioâ. The ontology is modelled and validated in protĂ©gĂ© software and exported to OWL API where the logical scenarios are generated and validated. An innumerable number of âconcreteâ scenarios are generated as a result of the possible combinations of the values from the domains of each conceptâs attributes. This research puts forward a novel genetic- algorithm (GA) approach to search through the scenario space and filter out safety critical test scenarios. A critical NLOS scenario is one where a collision is highly likely because the ego vehicle was unable to detect an obstacle in time due to obstructions present in the line-of-sight of the sensors or created due to the road geometry. The metric proposed to identify critical scenarios which also acts as the GAâs fitness function uses the time-to-collision (TTC) and total stopping time (TST) metric. These generated critical scenarios and proposed fitness function have been validated through MATLAB simulation. Furthermore, this research incorporates the relevant knowledge of vehicle-to-vehicle (V2V) communication technologies in the proposed ontology and uses the communication layer instances in the MATLAB simulation to support the testing of the increasing number of approaches that uses communications for alerting oncoming vehicles about imminent danger, or in other word, mitigating an otherwise critical scenario
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
32. Forum Bauinformatik 2021
Das Forum Bauinformatik ist eine jĂ€hrlich stattfindende Tagung und ein wichtiger Bestandteil der Bauinformatik im deutschsprachigen Raum. Insbesondere Nachwuchswissenschaftlerinnen und -wissenschaftlern bietet es die Möglichkeit, ihre Forschungsarbeiten zu prĂ€sentieren, Problemstellungen fachspezifisch zu diskutieren und sich ĂŒber den neuesten Stand der Forschung zu informieren. Es bietet sich ausgezeichnete Gelegenheit, in die wissenschaftliche Gemeinschaft im Bereich der Bauinformatik einzusteigen und Kontakte mit anderen Forschenden zu knĂŒpfen