Virtual testing of automated vehicles using simulations is essential during their development. When it comes to the testing of motion planning algorithms, one is mainly interested in challenging, critical scenarios for which it is hard to find a feasible solution. However, these situations are rare under usual traffic conditions, demanding an automatic generation of critical test scenarios. We present an approach that automatically generates critical scenarios based on a minimization of the solution space of the vehicle under test. By formulating a scenario parametrization and automatic determination of relevant parameter intervals, we are able to optimize the criticality of complex scenarios. We use evolutionary algorithms to tackle the resulting highly nonlinear optimization problem. Compared to our previous approach, we are now able to handle complex situations, in particular those involving intersections. Finally, we demonstrate our approach by generating critical scenarios from initially uncritical scenarios.

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms.

In a large-scale wireless sensor network, it is typically impossible to use all the sensors to localize a source due to the limited communication range and battery power of the sensor nodes, thereby a sensor selection procedure is required. In this paper, the sensor selection problem for time-difference-of-arrival (TDOA) based localization is addressed. We propose two nearly optimal sensor selection methods: one is based on the original TDOA model and the other is based on the transformed time-of-arrival (TOA) model. Both methods aim to minimize the trace of the Cramer-Rao lower bound (CRLB) under the condition of fixed number of selected sensors. For the first method, we introduce a special vector to distinguish the reference sensor with the non-reference sensors. We then formulate a non-convex optimization problem with a Boolean vector as the optimization variable. For the second method, we first prove that the CRLB for TDOA-based localization is exactly the same with that for TOA-based localization where the start transmission time is unknown. We then formulate a non-convex optimization problem to minimize the CRLB for TOA-based localization. Both non-convex optimization problems are relaxed as convex semidefinite programs (SDPs) by applying semidefinite relaxation, followed by the Gaussian randomization procedure to improve the quality of the SDP solutions if necessary. Simulation results show that the performance of proposed methods approaches that of the exhaustive search method.

Nearly Optimal Sensor Selection for TDOA-Based Source Localization in Wireless Sensor Networks

Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning, and control, which brings together the latest advances in different domains. Despite these achievements, safety assurance of ADS is of great significance, since unsafe behavior of ADS can bring catastrophic consequences. Testing has been recognized as an important system validation approach that aims to expose unsafe system behavior; however, in the context of ADS, it is extremely challenging to devise effective testing techniques, due to the high complexity and multidisciplinarity of the systems. There has been great much literature that focuses on the testing of ADS, and a number of surveys have also emerged to summarize the technical advances. Most of the surveys focus on the system-level testing performed within software simulators, and they thereby ignore the distinct features of different modules. In this paper, we provide a comprehensive survey on the existing ADS testing literature, which takes into account both module-level and system-level testing. Specifically, we make the following contributions: (1) we survey the module-level testing techniques for ADS and highlight the technical differences affected by the features of different modules; (2) we also survey the system-level testing techniques, with focuses on the empirical studies that summarize the issues occurring in system development or deployment, the problems due to the collaborations between different modules, and the gap between ADS testing in simulators and the real world; (3) we identify the challenges and opportunities in ADS testing, which pave the path to the future research in this field.

https://dl.acm.org/doi/pdf/10.1145/3579642

A Survey on Automated Driving System Testing: Landscapes and Trends

Making an informed and right decision poses huge challenges for drivers in day-to-day traffic situations. This task vastly depends on many subjective and objective factors, including the current driver state, her destination, personal preferences and abilities as well as surrounding environment. In this paper, we present CoSI (Context and Situation Intelligence), a Knowledge Graph (KG)-based approach for fusing and organizing heterogeneous types and sources of information. The KG serves as a coherence layer representing information in the form of entities and their inter-relationships augmented with additional semantic axioms. Harnessing the power of axiomatic rules and reasoning capabilities enables inferring additional knowledge from what is already encoded. Thus, dedicated components exploit and consume the semantically enriched information to perform tasks such as situation classification, difficulty assessment, and trajectory prediction. Further, we generated a synthetic dataset to simulate real driving scenarios with a large range of driving styles and vehicle configurations. We use KG embedding techniques based on a Graph Neural Network (GNN) architecture for a classification task of driving situations and achieve over 95% accuracy whereas vector-based approaches achieve only 75% accuracy for the same task. The results suggest that the KG-based information representation combined with GNN are well suited for situation understanding tasks as required in driver assistance and automated driving systems.

A Knowledge Graph-Based Approach for Situation Comprehension in Driving Scenarios

Congestion is an ongoing problem for many urban centres worldwide (such as London), leading to excessive delays, noise and air pollution, frustrated drivers, and high energy consumption. The carbon footprint of conventional transport systems can be high as a result and transport is among the highest contributors of greenhouse gas emissions. Therefore, with the growing interest in developing connected fully autonomous vehicles (ConFAVs), there is a pressing need to consider their effects within the congested urban setting. To address this, the current research study was designed to investigate the potential for ConFAVs in providing a sustainable transport solution. During this research, a simulation model was developed, calibrated, and validated using field data collected from several sites in East London, using the graphical user interface (GUI) simulation software PTV VISSIM to simulate the proposed driving and car following behaviour, which included the platooning of these ConFAVs, to assess how they could improve the level of service of the roads. Using the new model, this research addresses the shortcomings of two other adaptations of the Wiedemann 99 car-following models by changing the ConFAV’s behaviour to be more cautious when travelling behind a human driven vehicle, and less cautious when behind another ConFAV. As little is known about the transitional period from zero autonomy to full autonomy on the already congested road network, due to the fact that these vehicles are typically tested in small numbers (often one at a time in a controlled environment), the present research study introduced ConFAVs to the simulated network gradually and in large numbers at 20% intervals (namely 0% where there are no ConFAVs, 20%, 40%, 60%, 80%, and finally 100% where all vehicles within the network were ConFAVs). The average delays and subsequent level of service for the roads within the networks were then assessed against each ConFAV penetration level. This helped understand how the network’s efficiency changes when the number of ConFAVs increases, and the potential benefits for these self-driving vehicles on congestion and the ensuing greenhouse gas emissions. The model showed that a reduction in delay of up to 100% can be achieved by introducing ConFAVs, which translates to a significant reduction in greenhouse gas emissions. This, coupled with the fact that ConFAVs are predominantly electric, points to a future sustainable road transport system. The primary purpose of this research would be to investigate the potential of ConFAVs in reducing traffic congestion and, as a result, greenhouse gas emissions.

/pdf/potential-of-connected-fully-autonomous-vehicles-in-reducing-2kva6nie.pdf

Potential of Connected Fully Autonomous Vehicles in Reducing Congestion and Associated Carbon Emissions

In this paper, an ontology-based driving scene modeling, situation assessment and decision making method for autonomous vehicles in urban environment is proposed. Firstly, an ontology is developed to model the driving scene of urban environment and represent driving knowledge in machine-readable format. Then, a general deterministic situation assessment approach which assesses the safety of eight regions around the autonomous vehicle and legitimacy, reasonableness for changing to adjacent lanes is proposed. Based on the assessment results, the prolog reasoner is employed to generate reasonable behavior decisions. Field tests are conducted and the experimental results show that the proposed method can give effective decisions in different urban scenarios.

Ontology-Based Driving Scene Modeling, Situation Assessment and Decision Making for Autonomous Vehicles

The localization information is the premise of robot autonomous navigation. In this paper, intelligent vehicle is used as a platform for the present study. We perceive motion information and environment signpost information through wheel speed sensor and laser infrared radar SICK respectively, and realize the global localization of the vehicle with the fusion of the particle filter algorithm. The experimental result shows that when the environment signpost position is known, the global positioning error can be less than 1m.

Research on wide range localization for driverless vehicle in outdoor environment based on particle filter

As the development of the autonomous vehicles, the test and evaluation of the autonomy of autonomous vehicles has receiving considerable attentions from both academic and industrial research fields recently. How to rapid identify the performance boundaries of autonomous vehicles and then to accelerate the testing has been considered as one of the most important problems. Simulation test and challenging scenarios are considered as two effective ways to solve above problems. In this research, a framework of rapid generation of challenging simulation scenarios based on adversarial test was proposed. An optimization model based on newly developed indicators, i.e., safety, smoothness, sharpness and smartness, was proposed to assess the autonomy of autonomous vehicles. Due to the high dimensionality of the configuration space and computation complexity of the optimization problem, a quantum genetic algorithm was employed to solve the optimization problem owing to its high search ability and lower population size required. A simulation system based on Prescan, Carsim and Simulink was built to validate the effectiveness of the proposed method. Comparative experiments also were conducted to illustrate the efficiency of the proposed optimization algorithm.

Rapid Generation of Challenging Simulation Scenarios for Autonomous Vehicles Based on Adversarial Test

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Ontology-Based Driving Scene Modeling, Situation Assessment and Decision Making for Autonomous Vehicles

Research on wide range localization for driverless vehicle in outdoor environment based on particle filter

Rapid Generation of Challenging Simulation Scenarios for Autonomous Vehicles Based on Adversarial Test