Artificially Intelligent Rapid Rescue Vehicle System

<div class="section abstract"><div class="htmlview paragraph">New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS’ safety critical components; e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework’s elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS’ components, and different types of input attributes to these MLA are proposed for some basic ADAS’ components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-Learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS’ components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology.</div><div class="htmlview paragraph">ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision.</div><div class="htmlview paragraph">There are number of open source annotated datasets available viz. COCO, KITTI etc. But these datasets are limited to either US or European road environment scenarios. There is hardly any dataset available for Indian Specific road conditions. In order to capture all the India Specific objects on road, we are capturing data through various environmental conditions, road conditions and annotating objects on the collected camera images. 2-D bounding boxes are being used to annotate objects in an image.</div><div class="htmlview paragraph">These annotated images will then be used to train a machine learning model. Finally this trained model will be then used to detect Indian specific objects with real time data acquired through camera sensors mounted on the vehicle. Object detection is one of the prime aspects for ADAS functionality. The complete process will demonstrate end to end solution for object detection techniques from data generation to real-time object identification and recognition.</div></div>

<div class="htmlview paragraph">Digital maps have large potential to enhance or enable Advanced Driver Assistance Systems (ADAS) by extending driver horizon and therefore to contribute to safer, smarter and cleaner road transport. As a predictive sensor called ADAS Horizon, in-vehicle digital maps are an important source of information providing look-ahead capability for ADAS applications and providing further information for on-board sensors to enhance environment perception.</div> <div class="htmlview paragraph">This paper reports on results from the European Commission project MAPS&ADAS, part of PReVENT Integrated Project, dedicated to the development, test and validation of a standardized interface (ADAS Interface) between ADAS applications and ADAS map data sources for accessing map data regarding vehicle position.</div>

<div class="section abstract"><div class="htmlview paragraph">As a result of the development of Event Data Recorders (EDR) and the recent FMVSS regulation 49 CFR 563, today’s automobiles provide a limited subset of electronic data measurements of a vehicle’s state before and during a crash. Prior to this data, the only information available about the vehicle movements before or during a collision had come from physical evidence (e.g. tire marks), witnesses, aftermarket camera systems on vehicles, and ground-based cameras that were monitoring vehicle traffic or used for security surveillance. Today’s vehicles equipped with Advanced Driver Assistance Systems (ADAS) have vehicle-based sensors that measure information about the environment around a vehicle including other vehicles, pedestrians, and fixed wayside objects. Vehicles equipped with these ADAS systems use primarily radar, lidar, ultrasonic, and/or image sensors either in standalone operation or in combination to establish the range and movement of potential hazardous objects (e.g. other vehicles, poles, and pedestrians) around a vehicle. The data from these sensors is unique because it measures objective environment information surrounding a vehicle, which can play a role in reconstructing and understanding the contributing factors of an accident. As more vehicles become equipped with advanced safety systems and the requisite sensors for automated vehicles, accident investigators will need to become familiar with system functionality, the data that the sensors measure and may record, as well as the limitations of that data in order to effectively and accurately analyze a crash.</div><div class="htmlview paragraph">This paper reviews the sensors in ADAS collision avoidance systems that may be present on current vehicles and how the data from a vehicle based radar may be used to reconstruct a collision. A radar sensor mounted on a host vehicle is experimentally evaluated using stationary objects, and a stationary and moving radar. The results demonstrate how an investigator would use the radar data to recognize a fixed object in the environment and analyze the relative position with respect to the host vehicle. The analysis of this radar data includes the limitations, accuracy, and validation of the particular radar sensor data. In addition, the authors propose in general how data from radars could be used to elucidate the location of fixed objects in the environment surrounding a vehicle for the purpose of understanding an accident.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Advanced driver assistance systems (ADAS) and automated driving systems (ADS) continue to expand into the market at a rapid pace. As improved (i.e., next generation) versions of these systems become available, they will continue to face many challenges in their implementation and benefits for safety and driving operations. The solution will involve many parties, including road safety professionals and researchers who see the potential in these systems but may have difficulties keeping up with them, and safety advocates who are calling for these systems to achieve higher levels of safety now.</div><div class="htmlview paragraph"><b>The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics</b> explores these challenges that will fall on the National Highway Traffic Safety Administration (NHTSA) and automakers as they balance costs and benefits; establish reasonable regulations and standards; and determine how to improve, test, deliver, and use these systems successfully. Perhaps the most formidable challenge will be overcoming the expectation that ADAS—and especially ADS—will always work perfectly in every scenario.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

<div class="section abstract"><div class="htmlview paragraph">Automotive industry is inclined towards connected, comfortable, environment friendly, efficient and smarter systems. Advanced Driver Assist System (ADAS) technology assist drivers to achieve a safer as well as better ride by automation and improvisation of the vehicular systems. With the advent of ADAS system, there is a significant focus not only in the development of Electronic Control Units (ECUs) and its features to cater to the emerging market but also on the information that could be displayed to meet the functional as well as safety requirements. This ADAS information display ensures timely notification to the driver with unique alerts that can be acoustic or visual. These systems should be tested thoroughly to ensure reliability as failures may impose severe risk on the OEM. Hardware in the loop testing has been largely adopted by industry against manual testing in lieu of the testing constraints imposed by the latter. This paper discusses on the existing test methods for ADAS information display system testing using HIL. It also details on the automation trends adopted and highlights the key challenges associated with the development as well as reusability of the conventional test methods. It discusses an Artificial Intelligence based strategy for automation of cluster objects teaching, evaluation and illustrates the adoption of the same for ADAS HIL testing and brings out the efficiency and benefits achieved by the proposed strategy.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The testing of vision sensor-based Advanced Driver Assistance (ADA) systems and Electronic Control Units (ECUs) is quickly becoming a major requirement in the automotive industry. ADA ECU development teams require a verification and validation (V&V) infrastructure early in the development cycle, and traditional approaches are not suitable for this purpose. This paper proposes the design of an integrated test suite for the functional and real-time testing of ADA algorithms and ECUs. The proposed test suite consists of a hardware bridge built using a Field Programmable Gate Array (FPGA), a Universal Serial Bus (USB) 3.0 interface, and a software application running on the Personal Computer (PC). By using an FPGA to build the hardware bridge, we get a compact, real-time, and reconfigurable platform to support the desired test requirements. The USB3.0 interface is specifically proposed to support the high data rates evident in ADA systems based on multiple vision sensor and sensor fusion technologies. The PC-based software application has a modular, extensible, and scalable architecture to support current and future test requirements, as well as several features that improve the end user's productivity. The proposed test suite can be used for: (a) acquisition and storage of sensor data (recording mode), (b) visualization of stored sensor data (replay mode), (c) offline evaluation and improvement of ADA algorithms (functional test mode), (d) online testing of advanced driver assistance system (ADAS) ECUs (real-time test mode). The major contribution of this paper is the design proposal for supporting the real-time test mode for laboratory testing of ADA ECUs, and it concludes with a qualitative discussion of potential enhancements.</div></div>

<div class="section abstract"><div class="htmlview paragraph">A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.</div><div class="htmlview paragraph">This paper documents an extensive simulation and experimental campaign that provides a systematic quantification of fuel economy and mobility benefits resulting from leveraging Vehicle-to-Everything (V2X) connectivity, longitudinal (Level 1+) automation, and advanced cylinder deactivation to improve the propulsion system and vehicle efficiency. A Vehicle Dynamics and Powertrain (VD&PT) optimization framework, developed jointly by The Ohio State University and BorgWarner, utilizes advanced route information available from the navigation system and GPS, and Vehicle-to-Infrastructure (V2I) communication when available to minimize the cumulative fuel consumption over a driver-selected route.</div><div class="htmlview paragraph">To assess the benefits of these technologies in real-world conditions, a Monte Carlo simulation framework was developed to quantify the impact of Signal Phase and Timing (SPaT), variability in traffic conditions, and behavior of different drivers on the fuel consumption and vehicle travel time. Further, verification through in-vehicle testing was conducted by reconstructing a subset of these scenarios on a proving ground.</div></div>

<div class="section abstract"><div class="htmlview paragraph">With the proliferation of ADAS and autonomous systems, the quality and quantity of the data to be used by vehicles has become crucial. In-vehicle sensors are evolving, but their usability is limited to their field of view and detection distance. V2X communication systems solve these issues by creating a cooperative perception domain amongst road users and the infrastructure by communicating accurate, real-time information.</div><div class="htmlview paragraph">In this paper, we propose a novel Consolidated Object Data Service (CODS) for multi-Radio Access Technology (RAT) V2X communication. This service collects information using BSM packets from the vehicular network and perception information from infrastructure-based sensors. The service then fuses the collected data, offering the communication participants with a consolidated, deduplicated, and accurate object database. Since fusing the objects is resource intensive, this service can save in-vehicle computation costs. The combination of diverse input sources improves the object detection accuracy, which can benefit the vehicle's ADAS or autonomous driving functions.</div><div class="htmlview paragraph">A testbed was developed to evaluate the performance of the system under three network architectures – local RSU, Edge and the Cloud. The CODS resided in virtual machines in the corresponding three locations. The OBUs and RSU used had multi-RAT (C-V2X PC5 and 5G Uu) connectivity. A connected thermal camera was used as the infrastructure sensor in our setup.</div><div class="htmlview paragraph">The paper presents the performance evaluation of various CODS realizations, deployment details of the testbed on a live network and introduces our promising experimental results, explaining the trade-offs of the different deployment schemes and their effects on system fidelity and communication characteristics.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Acceptance criteria and validation target are the most important metrics used to measure/assure safety of the intended function (SOTIF) of an autonomous vehicle or advanced driver assistance system (ADAS). Often acceptance criteria are defined as acceptable number of fatalities, injuries or property damage events in certain hours of operation or for certain mileage driven. Validation target on the other hand is the amount of effort required in terms of hours of operation or mileage to be driven to show that the acceptance criteria is met. Although existing research details about potential values for acceptance criteria and validation target, they overlook various factors such as operational design domain, operational lifetime of a vehicle, average mileage of a vehicle, and length of roads. As a result, often acceptance criteria values are very small (e.g., 10<sup>-12</sup> incidents/h or mi) and validation targets are very large (e.g., 10<sup>13</sup> miles). However, these targets may not be practical to distribute within various testing avenues/platforms and provide as evidence for a given version. In this paper, we discuss the various factors and strategies we can consider for defining reasonable and practically achievable acceptance criteria and validation targets. For example, the number of miles driven by motor vehicles in United States in 2020 and the number of accidents occurred in those miles is given based on distance travelled by around 276 million vehicles. Will the distance and number of accidents remain the same even as the fleet size is 100 vehicles? In this paper, we aim to resolve such questions. Using an illustrative example, we demonstrate the usefulness of our proposed factors and strategies.</div></div>

<div class="section abstract"><div class="htmlview paragraph">In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics.</div><div class="htmlview paragraph">The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems.</div><div class="htmlview paragraph">Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle. Some high precision sensors were mounted in both test vehicles, such as RT 3002<sup>®</sup>, RT range<sup>®</sup>, radar and pedal pressure sensor. The traffic vehicle was set in different driving modes, include cruise mode, decelerating slowly, accelerating slowly, hard braking, hard acceleration, cutting out the lane, cutting into the line and sinusoidal movement. The host vehicle was operated behind the traffic vehicle by 84 drivers selected randomly. The drivers’ driving styles were evaluated via both subjective and objective indicators. The subjective indicators were based on the questionnaires by interviewing the drivers and the passengers. The objective indicators were based on the parameters extracted from the specific test conditions. The vehicle-following data were collected and analyzed via MATLAB<sup>®</sup> which was further trained by Gaussian Mixture Model. With regarding to the multi-mode switching strategy, the parameters of different driver’s characteristics were considered in the fuzzy PID control method to improve the adaptivity and accuracy in the switching process. Meanwhile, different fuzzy rules and fuzzy membership functions were applied for every type of drivers to calculate the best parameters for the personalized ACC control strategy. The control modes include cruise, steady following, approaching and hard braking. The different driving characteristics were also added into every control strategy via road experimental data and analyzed results from Gaussian Mixture Model. Compared with the existing ACC, the system proposed in this paper has great advantages on not only the adaptivity and human acceptance, but also the performance of the system itself which was verified on the simulation platform PanoSim<sup>®</sup>.</div></div>

<div class="section abstract"><div class="htmlview paragraph">In recent years, the demand for high-speed/high-bandwidth communication for in-vehicle networks has been increasing. This is because the usage of high-resolution screens and high-performance rear seat entertainment (RSE) systems is expanding. Additionally, it is also due to the higher number of advanced driver assistance systems (ADAS) and the future introduction of autonomous driving systems. High-volume data such as high definition sensor images or obstacle information is necessary to realize these systems. Consequently, automotive Ethernet, which meets the requirements for high-speed/high-bandwidth communication, is attracting a lot of attention.</div><div class="htmlview paragraph">The application of automotive Ethernet to in-vehicle networks requires that technology developments satisfy EMC performance requirements. In-vehicle EMC requirements consist of two parts: emission and immunity. The emission requirement is to restrict the electromagnetic noise emitted from vehicle. Therefore, automotive PHY transceivers that suppress emission noise have been developed and are available on the market. The immunity requirement is resistance against electromagnetic radiation from inside or outside the vehicle. It is commonly known that immunity performance decreases when the impedance characteristics of the transmission lines are unbalanced. In the vehicle, the circuit pattern length of the ECU and the characteristics of the electronics parts used in the ECU affect the impedance balance of the transmission lines.</div><div class="htmlview paragraph">This paper proposes an ECU circuit design management technique using mixed mode S parameter characteristics. The correlation of the mixed mode S parameter characteristics with the resistance performance against immunity noise was validated, and the criteria for the 1000BASE-T1 PHY mixed mode S-parameters to satisfy this performance was defined.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called Vehicle-in-Virtual –Environment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed. The virtual environment can be easily configured and modified to construct different testing scenarios on demand. This paper focuses on the VVE approach and introduces the coordinate transformations needed to sync pose (location and orientation) in the virtual and physical worlds and handling of localization and perception sensor data using the highly realistic 3D simulation model of a recent autonomous shuttle deployment site in Columbus, Ohio as the virtual world. As a further example that uses multiple actors, the use of VVE for Vehicle-to-VRU communication based Vulnerable Road User (VRU) safety is presented in the paper using VVE experiments and real pedestrian(s) in a safe and repeatable manner. VVE experiments are used to demonstrate the efficacy of the method.</div></div>

<div class="section abstract"><div class="htmlview paragraph">This paper presents a cost effective development platform, named FEV-Driver, for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD). The FEV-Driver platform is an electric go-kart that was converted into an x-by-wire vehicle which represents the behavior of a full-scale electric vehicle. FEV-Driver has the advantage of being a small-scale vehicle that can be used with a significant lower safety risk compared to full-sized vehicles. The ADAS/AD algorithms for this platform were developed in both Simulink and C++ software and implemented within the Robot Operating System (ROS) middleware. Besides the description of the platform, Lane Keep Assist (LKA) and Automatic Emergency Braking (AEB) algorithms are discussed, followed by a path planning algorithm which enables the vehicle to drive autonomously after a manually controlled training lap. The modular system architecture allows for complete controller exchange or adaptation to different vehicles. The adaptation and implementation of the platform into a full-scale passenger vehicle is described in the last section of this paper. The presented platform has proven to be a low-cost scalable platform for development and verification of ADAS and AD functions.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance. In this paper, a vehicle trajectory prediction method based on motion model and maneuver model fusion with Interactive Multiple Model (IMM) is proposed. Firstly, Constant Turn Rate and Acceleration (CTRA) motion model and Unscented Kalman Filter (UKF) are used to predict vehicle trajectory with uncertainty in the future. Then, vehicle trajectory prediction based on simplified maneuver recognition model is conducted, using temporal and spatial relationship between vehicle historical trajectory and lane lines. After that, vehicle trajectory prediction by integrating motion model and maneuver model with IMM is conducted. Finally, the proposed method is compared with CTRA motion model based vehicle trajectory prediction and lane keeping model (LKM) based vehicle trajectory prediction in two simulation test scenarios. The simulation results indicates that the IMM-based method achieves both excellent prediction accuracy and appropriate prediction uncertainty in the whole prediction horizon. This research can be used to support decision making for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems and leads to improvement of traffic safety.</div></div>