Driver Assistance Applications Research Articles

Evaluating the impact of microservice-centric computations in internet of vehicles

This paper presents the design and implementation of a hierarchical and multilayered computing framework, specifically tailored to analyze the benefits of Named Data Networking (NDN) enabled microservice-centric in-network computations in autonomous vehicular networks. The proposed framework comprises three layers: an autonomous vehicular network layer, an Edge computing layer, and a centralized Cloud.Modifications were made to the vanilla NDN codebase to facilitate communication between the vehicular network layer, Edge computing and Cloud layer, utilizing the C++ Boost Asio library and IP tunneling mechanism. A microservices-based driver assistance application, consisting of various heterogeneous microservices, was emulated in .NET Core and deployed on different Edge computing terminals and the Cloud. Additionally, RESTful APIs have been developed to enable the vehicular network layer and the physical Edge layer to offload microservice-centric computation requests and retrieve the corresponding computational outcomes. The Entity framework is employed to ensure proper tracking and management of these requests. An HTML-based user interface has also been developed for a visual representation of the request pattern. Extensive testbed experiments reveal that the proposed system significantly optimizes bandwidth consumption, reduces latency, and increases the computed results delivery ratio when compared to conventional monolithic systems.

Real-Time Modeling of Vehicle’s Longitudinal-Vertical Dynamics in ADAS Applications

The selection of an appropriate method for modeling vehicle dynamics heavily depends on the application. Due to the absence of human intervention, the demand for an accurate and real-time model of vehicle dynamics for intelligent control increases for autonomous vehicles. This paper develops a multibody vehicle model for longitudinal-vertical dynamics applicable to advanced driver assistance (ADAS) applications. The dynamic properties of the chassis, suspension, and tires are considered and modeled, which results in accurate vehicle dynamics and states. Unlike the vehicle dynamics models built into commercial software packages, such as ADAMS and CarSim, the proposed nonlinear dynamics model poses the equations of motion using a subset of relative coordinates. Therefore, the real-time simulation is conducted to improve riding performance and transportation safety. First, a vehicle system is modeled using a semi-recursive multibody dynamics formulation, and the vehicle kinematics and dynamics are accurately calculated using the system tree-topology. Second, a fork-arm removal technique based on the rod-removal technique is proposed to reduce the number of bodies, relative coordinates, and equations constrained by loop-closure. This increase the computational efficiency even further. Third, the dynamic simulations of the vehicle are performed on bumpy and sloping roads. The accuracy and efficiency of the numerical results are compared to the reference data. The comparative results demonstrate that the proposed vehicle model is effective. This efficient model can be utilized for the intelligent control of vehicle ADAS applications, such as forward collision avoidance, adaptive cruise control, and platooning.

Road Sensing with Intelligent Tires for Driving Assistance Applications

ABSTRACT Tires with embedded sensors enable them to be active sensing components. Intelligent tires have increased potential in sensing not only the state of the tire itself but the related properties of the road. This Technical Note briefly describes the application of accelerometer-based intelligent tires in sensing the adhesion level and unevenness of the road. A combined tire longitudinal dynamic model is introduced to estimate the tire-road friction from the waveform features of the acceleration signals. Data-driven methods are adopted to directly classify the road unevenness level from the original signals. Primary analyses and experiments verify the proposed method. Road sensing with intelligent tires contributes to the development of active safety control and driving assistance systems.

Smart Application for Every Car (SAEC). (AR Mobile Application)

Technology is continuously evolving at an exponential rate. Fast technological advances are being made, especially in the field of smart phones, that facilitate the conduct of our daily activities in many areas such as driving. The ever-increasing number of vehicles on roads increases the likelihood of traffic accidents, resulting in higher number of deaths and serious injuries to drivers, passengers, and pedestrians. Among the main causes of road accidents are over speeding, unsafe lane jumping, and failure to keep a safe distance between vehicles, to name a few. In an attempt to contribute to the improvement of road traffic safety, we have developed an Augmented Reality-based Smart Vehicle Driver Assistance application. The application is designed to enhance vehicle driver’s safety, in particular, but is also considered to lead to incremental improvement of safety of road traffic. The application can run on both Android and iOS platforms and incorporates several beneficial features required by a vehicle driver such as monitoring of vehicle speed, warning the driver in case of lane deviation, detection of road signs, and to alert the driver if the vehicle is not being driven at a safe distance from the vehicle in front. In addition to providing information to improve safe driving, the application also helps the vehicle driver save parking location of the vehicle in order to efficiently identify the parking location when retrieving the vehicle. This feature is very useful at large and unfamiliar parking areas, such as at airports or one-off large public gatherings, especially in inclement weather. The application also includes other useful functions such as the payment of parking fees, storage of information regarding vehicle maintenance, and keeping the vehicle legal document up to date. The application uses the stored information to display reminders of the appropriate action that needs to be taken before it becomes overdue.

A collection of easily deployable adversarial traffic sign stickers

Abstract The emergence of new autonomous driving systems and functions – in particular, systems that base their decisions on the output of machine learning subsystems responsible for environment perception – brings a significant change in the risks to the safety and security of transportation. These kinds of Advanced Driver Assistance Systems are vulnerable to new types of malicious attacks, and their properties are often not well understood. This paper demonstrates the theoretical and practical possibility of deliberate physical adversarial attacks against deep learning perception systems in general, with a focus on safety-critical driver assistance applications such as traffic sign classification in particular. Our newly developed traffic sign stickers are different from other similar methods insofar that they require no special knowledge or precision in their creation and deployment, thus they present a realistic and severe threat to traffic safety and security. In this paper we preemptively point out the dangers and easily exploitable weaknesses that current and future systems are bound to face.

GazMon: Eye Gazing Enabled Driving Behavior Monitoring and Prediction

Automobiles have become one of the necessities of modern life, but also introduced numerous traffic accidents that threaten drivers and other road users. Most state-of-the-art safety systems are passively triggered, reacting to dangerous road conditions or driving maneuvers only after they happen and are observed, which greatly limits the last chances for collision avoidances. Timely tracking and predicting the driving maneuvers calls for a more direct interface beyond the traditional steering wheel/brake/gas pedal. In this paper, we argue that a driver's eyes are the interface, as it is the first and the essential window that gathers external information during driving. Our experiments suggest that a driver's gaze patterns appear prior to and correlate with the driving maneuvers for driving maneuver prediction. We accordingly present GazMon, an active driving maneuver monitoring and prediction framework for driving assistance applications. GazMon extracts the gaze information through a front-camera and analyzes the facial features, including facial landmarks, head pose, and iris centers, through a carefully constructed deep learning architecture. Both our on-road experiments and driving simulator based evaluations demonstrate the superiority of our GazMon on predicting driving maneuvers as well as other distracted behaviors. It is readily deployable using RGB cameras and allows reuse of existing smartphones towards more safely driving.

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

Driver Danger-Level Monitoring System Using Multi-Sourced Big Driving Data

Danger-level analysis is widely used to prevent potential driving risks based on driving performance. Such analysis is essential for monitoring driver performance. Moreover, danger-level analysis is vital for automotive safety systems and driving assistance applications. However, danger-level analysis that simultaneously considers driver-, vehicle-, and road-related information from driving data has rarely been conducted. Such analysis is very challenging due to the issues associated with the high volume and high variety in multisourced driving data. In this paper, we propose a novel danger-level analysis framework for dealing with high variety and high volume problems of multisourced driving data. Built upon a feature extraction method in the proposed framework, we first profile multisourced driving features for overcoming the variety problem. Next, danger-level analysis is formulated as a multiobjective pursuit problem in a linear model. The problem is then solved using a semisupervised learning strategy to overcome the volume issue. Therefore, the danger level can be accurately estimated from multisourced driving data by using the proposed framework. The experimental results indicate that the proposed framework outperforms existing machine learning techniques for multisourced driving data.

An Advanced Driver Risk Measurement System for Usage-Based Insurance on Big Driving Data

Usage-based-insurance is an emerging automobile insurance service in which the driver premium is set individually for each policyholder. A personalized automobile insurance mechanism presents challenges that differ from those presented by the general driver assistance applications that analyze driver behaviors. In this paper, a novel framework based on boosted multiple-kernel learning is proposed to reflect the driving risk level of each driver for automobile usage-based-insurance. In the proposed framework, a set of kernels is specified to represent the inherent characteristics of vehicle-oriented, driver-oriented, and lane-oriented attributes. These multiple kernels are carefully integrated using the AdaBoost technique to realize particular collaborative features for driving risk assessment. Experimental results obtained using a lab-recorded driving data set under real-world conditions reveal that the proposed framework exhibits impressive accuracy and robustness in terms of different driving-risk levels.

Robust detection and recognition of traffic signs on road panels

Traffic panels provide vital information on roads with the aid of iconic symbols and text strings. Recognising these signs accurately at the right time is crucial for car drivers to ensure safe journey. The automatic visual recognition and classification of the information contained in the panel could be very useful for driver assistance application. In this paper, a method is proposed to identify and to recognise the information contained in the traffic panels, as an application utilising colour space conversion for image acquisition then the colour decomposition and shape model generation using active appearance model (AAM). The traffic panel is detected using character descriptor-based adaptive fuzzy clustering (AFC). Multiframe detection strategy is a simple way of using precision and recall. Finally the prediction accuracy is computed by using massive training artificial neural network (MTANN). The efficiency of the system is evaluated with the help of the MATLAB-based experimental results which are compared with the bag of visual words for text geolocation and recognising symbols and texts (BTG & ST) method and shape and colour (SC)-based traffic sign detection methods in terms of the sensitivity, specificity and recognition rate.

Robust detection and recognition of traffic signs on road panels

Traffic panels provide vital information on roads with the aid of iconic symbols and text strings. Recognising these signs accurately at the right time is crucial for car drivers to ensure safe journey. The automatic visual recognition and classification of the information contained in the panel could be very useful for driver assistance application. In this paper, a method is proposed to identify and to recognise the information contained in the traffic panels, as an application utilising colour space conversion for image acquisition then the colour decomposition and shape model generation using active appearance model (AAM). The traffic panel is detected using character descriptor-based adaptive fuzzy clustering (AFC). Multiframe detection strategy is a simple way of using precision and recall. Finally the prediction accuracy is computed by using massive training artificial neural network (MTANN). The efficiency of the system is evaluated with the help of the MATLAB-based experimental results which are compared with the bag of visual words for text geolocation and recognising symbols and texts (BTG & ST) method and shape and colour (SC)-based traffic sign detection methods in terms of the sensitivity, specificity and recognition rate.

Development and Evaluation of High-Speed Differential Warning Application Using Vehicle-to-Vehicle Communication

Connected vehicle (CV) technology has shown great potential in enhancing safety, mobility, and environmental sustainability of roadway transportation systems by enabling vehicles and infrastructure to exchange real-time information through wireless communications. The information available through CV technology has been used to develop a variety of onboard driver assistance tools and applications. This paper presents an innovative, safety-focused CV application called the high-speed differential warning (HSDW). The application identifies potential hazards resulting from high-speed differentials between HSDW-equipped vehicles (ego-vehicles) and surrounding remote vehicles and then provides alerts to drivers of ego-vehicles to help them take appropriate actions. In this research, real-world scenarios and parameters of the HSDW application are first identified. A driver response strategy for each scenario is then developed in consonance with findings from a public survey on the HSDW application. Next, a comprehensive evaluation of various scenarios is conducted with a well-calibrated Paramics traffic microscopic simulation tool set up for a real-world traffic network. Finally, the safety performance of HSDW-equipped vehicles, unequipped vehicles, and overall traffic is analyzed with the surrogate safety assessment model. Results demonstrate that the proposed HSDW application improves the safety performance of ego-vehicles without compromising the mobility and environmental sustainability performance of the overall traffic.

C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems

Cyber-physical system (CPS) is a new trend in the Internet-of-Things related research works, where physical systems act as the sensors to collect real-world information and communicate them to the computation modules (i.e. cyber layer), which further analyze and notify the findings to the corresponding physical systems through a feedback loop. Contemporary researchers recommend integrating cloud technologies in the CPS cyber layer to ensure the scalability of storage, computation, and cross domain communication capabilities. Though there exist a few descriptive models of the cloud-based CPS architecture, it is important to analytically describe the key CPS properties: computation, control, and communication. In this paper, we present a digital twin architecture reference model for the cloud-based CPS, C2PS, where we analytically describe the key properties of the C2PS. The model helps in identifying various degrees of basic and hybrid computation-interaction modes in this paradigm. We have designed C2PS smart interaction controller using a Bayesian belief network, so that the system dynamically considers current contexts. The composition of fuzzy rule base with the Bayes network further enables the system with reconfiguration capability. We also describe analytically, how C2PS subsystem communications can generate even more complex system-of-systems. Later, we present a telematics-based prototype driving assistance application for the vehicular domain of C2PS, VCPS, to demonstrate the efficacy of the architecture reference model.

Generic Dynamic Environment Perception Using Smart Mobile Devices.

The driving environment is complex and dynamic, and the attention of the driver is continuously challenged, therefore computer based assistance achieved by processing image and sensor data may increase traffic safety. While active sensors and stereovision have the advantage of obtaining 3D data directly, monocular vision is easy to set up, and can benefit from the increasing computational power of smart mobile devices, and from the fact that almost all of them come with an embedded camera. Several driving assistance application are available for mobile devices, but they are mostly targeted for simple scenarios and a limited range of obstacle shapes and poses. This paper presents a technique for generic, shape independent real-time obstacle detection for mobile devices, based on a dynamic, free form 3D representation of the environment: the particle based occupancy grid. Images acquired in real time from the smart mobile device’s camera are processed by removing the perspective effect and segmenting the resulted bird-eye view image to identify candidate obstacle areas, which are then used to update the occupancy grid. The occupancy grid tracked cells are grouped into obstacles depicted as cuboids having position, size, orientation and speed. The easy to set up system is able to reliably detect most obstacles in urban traffic, and its measurement accuracy is comparable to a stereovision system.

MPSoC Software Debugging on Virtual Platforms via Execution Control with Event Graphs

Virtual Platforms (VPs) are advantageous to develop and debug complex software for multi- and many-processor systems-on-chip (MPSoCs). VPs provide unrivaled controllability and visibility of the target, which can be exploited to examine bugs that cannot be reproduced easily in real hardware (e.g., bugs originating from races or happening during a processor stand-by state). However, VPs as employed in practice for debugging are generally underutilized. The accompanying debug ecosystem is based mostly on traditional tools, such as step-based debuggers and traces, that fall short to address the enormous complexity of modern MPSoCs and their parallel software. Finding a bug is still largely left to the developer’s experience and intuition, using manual means rather than automated or systematic solutions that exploit the controllability and visibility of VPs. Profiting from VPs for MPSoC software debugging is an open question. To bridge this gap, this article presents a novel framework for debug visualization and execution control that, relying on the many benefits of VPs, helps to identify and test possible concurrency-related bug scenarios. The framework allows examining and steering the target system by manipulating an abstract graph that highlights relevant inter-component interactions and dependencies. The proposed framework reduces the effort required to understand complex concurrency patterns and helps to expose bugs. Its efficacy is demonstrated on (i) a shared memory symmetric multi-processing platform executing Linux and parallel benchmarks, and (ii) a distributed automotive system for driver assistance applications.

Self-Calibration of a 3-D-Digital Beamforming Radar System for Automotive Applications With Installation Behind Automotive Covers

Given the complexity of a radar system with digital beamforming, a number of error influences can appear due to hardware imperfections, temperature drift, and aging. These errors can result in a reduced target detection probability or even lead to false detections. For automotive driver assistance applications, there is a growing demand for hidden integration of radar systems. When installed behind a painted bumper or design radome, additional performance degradation may occur. First, the hardware imperfections according to their appearance in the presented radar system and their effects are analyzed with respect to the angular spectrum estimation. A software-based self-calibration algorithm is proposed and evaluated by means of simulation and measurement. Finally, the extended performance capability of the self-calibration procedure to compensate additional error components from automotive covers is investigated by means of a painted bumper in front of the radar sensor.

Multifunctional Transceiver for Future Radar Sensing and Radio Communicating Data-Fusion Platform

A unified and reconfigurable multifunctional transceiver for future integrated data-fusion services of radar sensing and radio communication (RadCom) is studied and developed in this paper. This proposed alternative of the state-of-the-art architectures presents an unprecedented integration of all radar sensing and RadCom functions together in a time-division platform. Furthermore, it is capable of offering a positioning function of both moving and static objects with an enhanced resolution in ranging in addition to providing a greater capability of data communication. The design and the performance incompatibilities between radar and radio systems are explored and investigated. A systematic top–bottom approach is presented, which involves the step-by-step methodology, building block design considerations, and the system level simulation. With the purpose of validating the proposed scheme, a low-frequency prototype around the FCC-commissioned dedicated short range communication (DSRC) band is developed, and its performance is evaluated. Since such a unified transceiver can find applications in intelligent transportation infrastructures, the system demonstrator is designed and examined according to the desired specifications of future automotive radar networks. Through various system level measurements, the proposed scheme has demonstrated attractive features in connection with both radar and radio functions. With the radar mode, the added ability of angle detection and the improved range resolution against the previously demonstrated version make the system suitable for driving assistance applications. With the radio mode, the system demonstrator has proved a great capability of communication at a data rate of 25 Mb/s.

Guest Editorial Special Section on Automotive Embedded Systems and Software

Today, most of the innovation in the automotive domain is in the areas of electronics and software. Modern cars have already been transformed, from largely mechanical entities, to complex embedded systems running on four wheels. High-end cars currently have around 100 electronic control units (ECUs), each with one or more, possibly multicore, processors. These ECUs communicate using different communication buses such as CAN, FlexRay, LIN, and more recently also Ethernet, and are connected to various cameras, radars, ultrasonic sensors, and also to a host of actuators. Such architectures are used to run several millions of lines of software code spanning across safety-critical, driver assistance, comfort, and entertainment related applications.

DMAP: Density Map Service in City Environments

Vehicle density information is crucial for efficient functioning of many vehicular applications, including emergency notification, driver assistance, and infotainment applications. This information is used for evacuation planning in accident scenarios, finding alternate routes in the case of road congestion, and providing stable routing paths for uninterrupted internet connections. In a city scenario, it is a tedious task to continuously collect and share large volumes of data containing density information. In this paper, we propose a mechanism to create a density map for city environments. A hierarchy (i.e., tree) is established, where a node represents a road segment and the density is used to determine the height of the node; the root node represents the road segment having highest density. The purpose of this tree is to collect and aggregate density information starting from the leaves until the root node is reached. Then, the aggregated density information (i.e., density map) is forwarded down the hierarchy. For efficient aggregation of density information, we adopt an effective curve-fitting method where data are represented in an equation. Simulation results show that the proposed mechanism allows highly accurate computing of density map while generating low network overhead.

Intra-Vehicle Networks: A Review

Automotive electronics is a rapidly expanding area with an increasing number of safety, driver assistance, and infotainment devices becoming standard in new vehicles. Current vehicles generally employ a number of different networking protocols to integrate these systems into the vehicle. The introduction of large numbers of sensors to provide driver assistance applications and the associated high-bandwidth requirements of these sensors have accelerated the demand for faster and more flexible network communication technologies within the vehicle. This paper presents a comprehensive overview of current research on advanced intra-vehicle networks and identifies outstanding research questions for the future.