Collision Avoidance Applications Research Articles

Railway Accident Prevention using Ultrasonic Sensor with Microcontrollers

Railway Transport is indispensable in modern day life, both for business and private users. Nowadays, rail networks across the world are getting busier with trains travelling at higher speeds and carrying more passengers and heavier axle loads than ever before. The combination of these factors has put considerable pressure on the existing infrastructure, leading to increased demands in inspection and maintenance of rail assets. But nowadays, it is not that much safer as lot of accidents occur due to improper communication among the network like wrong signalling , worst weather condition, immediate route change, etc., The train driver doesn‟t get proper information on time and before time so that the hazardous condition can occur. While maritime and air transport are already benefiting from collision avoidance application based on infrastructure less communications. We propose this system to avoid train collision by using Ultrasonic Sensors to provide communication between trains and to avoid same track collisions

Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking

This paper develops a multi-stage estimation algorithm for vehicle trajectory tracking applications. Previously designed nonlinear observers for vehicle trajectory tracking lack either the ability to handle variable velocity or have a high sensitivity to sensor noise. To overcome these shortcomings, the original model of the non-ego vehicle is translated into three separate models for speed, orientation, and position. Three stable observers are designed for these models which are all shown to be stable and robust to uncertainties. The new estimation algorithm outperforms previous high-gain and LMI-based nonlinear observers. The developed observer is useful for collision prediction and avoidance applications.

An Enhancement for IEEE 802.11p to Provision Quality of Service with Context Aware Channel Access for the Forward Collision Avoidance Application in Vehicular Ad Hoc Network.

Key application of an intelligent transportation system is traffic safety, and it provides driver assistance. Safety messages are of two types, beacon messages and event messages. The nodes broadcast these messages in the vehicular networks. The system must rely on a robust medium access control (MAC) protocol to support delivery of safety messages. The standard medium access scheme that is used in vehicular networks to provide service differentiation to support various applications is IEEE 802.11p. The emergency event messages should reach the drivers immediately to take necessary steps to avoid casualties on the road. In IEEE 802.11p, both of these messages are considered with the same priority so that no separate differentiation is created. The proposed work focuses on improving the quality of service for forward collision warning applications in intelligent transportation systems. The scheme proposes a priority-based cooperative MAC (PCMAC) for channel access that works on the context of information. Simulation and analytical results validate improved performance of PCMAC in terms of packet delivery ratio, throughput, and average packet delivery delay, as compared with other eminent MAC protocols. The simulation results show that it has a 9% higher improvement in throughput than IEEE 802.11p and has better performance in the increasing number of emergency messages.

Toward Optimal MEC Resource Dimensioning for a Vehicle Collision Avoidance System: A Deep Learning Approach

Collision detection and avoidance between vehicles is one of the key services envisioned in the Internet of Vehicles. Such services are usually deployed at the multi-access edge computing (MEC) to ensure low-latency communication and thus guarantee real-time reactions to avoid collisions between vehicles. In order to maximize the coverage of the road and ensure that all vehicles are connected to an optimal MEC host (in terms of geographical location), the collision avoidance application needs to be instantiated on all the MEC hosts. This may add a burden on the computing resources available at the latter. In this article, we propose an AI-empowered framework that aims to optimize the computing resources at the MEC hosts. Our framework uses deep learning to predict the vehicle density to be served by a MEC host and derive the exact computing resources required by the collision detection application to run optimally. We evaluate the proposed framework using a real dataset representing vehicle mobility in a big city. Obtained results show the accuracy of our prediction model, and hence the efficiency of our resources assignment framework to exactly deduce the optimal computing resources needed by each instance of the application.

VANET: Framework, Challenges and Applications

Abstract: Vehicular Ad-Hoc Network (VANET) is one of the essential research field because of large increase in usage of vehicles on road. Many VANET applications are employed to improve road safety conditions, vehicle traffic, an emergency warning to vehicle drivers, collision avoidance and other non-safety applications for comfort. The main aim of these applications are to modernize the various processes associated with road traffic, vehicles, drivers, passengers and pedestrians by implementing smart transport systems. This study is to implement an intelligent vehicular transport design to improve the road safety, navigation and comfort. VANET communication technology is a combination of mobile vehicles with ad hoc networks. The purpose of this research is to predict and prevent road accidents by transmitting emergency messages using vehicular network technology. While transmitting emergency messages, the VANET has various challenges. They are High mobility of the vehicles, Dynamic topology, Wireless communication, Minimum transmission delay, Connectivity of the network, optimal usage of transmission power. These challenges leave the vehicular networking disconnected and make an exchange of information very difficult. Even though many researchers have done significant work during the last decade on vehicular networking, some problems have not yet been solved.

VANET: Framework, Challenges and Applications

Vehicular Ad-Hoc Network (VANET) is one of the essential research field because of large increase in usage of vehicles on road. Many VANET applications are employed to improve road safety conditions, vehicle traffic, an emergency warning to vehicle drivers, collision avoidance and other non-safety applications for comfort. The main aim of these applications are to modernize the various processes associated with road traffic, vehicles, drivers, passengers and pedestrians by implementing smart transport systems. This study is to implement an intelligent vehicular transport design to improve the road safety, navigation and comfort. VANET communication technology is a combination of mobile vehicles with ad hoc networks. The purpose of this research is to predict and prevent road accidents by transmitting emergency messages using vehicular network technology. While transmitting emergency messages, the VANET has various challenges. They are High mobility of the vehicles, Dynamic topology, Wireless communication, Minimum transmission delay, Connectivity of the network, optimal usage of transmission power. These challenges leave the vehicular networking disconnected and make an exchange of information very difficult. Even though many researchers have done significant work during the last decade on vehicular networking, some problems have not yet been solved.

On Using a Low-Density Flash Lidar for Road Vehicle Tracking

Abstract This study uses a low-density solid-state flash lidar for estimating the trajectories of road vehicles in vehicle collision avoidance applications. Low-density flash lidars are inexpensive compared to the commonly used radars and point-cloud lidars, and have attracted the attention of vehicle manufacturers recently. However, tracking road vehicles using the sparse data provided by such sensors is challenging due to the few reflected measurement points obtained. In this paper, such challenges in the use of low-density flash lidars are identified and estimation algorithms to handle the same are presented. A method to use the amplitude information provided by the sensor for better localization of targets is evaluated using both physics-based simulations and experiments. A two-step hierarchical clustering algorithm is then employed to group multiple detections from a single object into one measurement, which is then associated with the corresponding object using a Joint Integrated Probabilistic Data Association (JIPDA) algorithm. A Kalman filter is used to estimate the longitudinal and lateral motion variables and the results are presented, which show that good tracking, especially in the lateral direction, can be achieved using the proposed algorithm despite the sparse measurements provided by the sensor.

A Dynamically Feasible Fast Replanning Strategy with Deep Reinforcement Learning

In this work, we aim to develop a fast trajectory replanning methodology enabling highly agile aerial vehicles to navigate in cluttered environments. By focusing on reducing complexity and accelerating the replanning problem under strict dynamical constraints, we employ the b-spline theory with local support property for defining the high dimensional agile flight trajectories. We utilize the differential flatness model of an aerial vehicle, allowing us to directly map the desired output trajectory into input states to track a high dimensional trajectory. Dynamically feasible replanning problem is addressed through regenerating the local b-splines with control point reallocation. As the geometric form of the trajectory based on the location of the control points and the knot intervals, the control point reallocation for fast replanning with dynamical constraints is turned into a constrained optimization problem and solved through deep reinforcement learning. The proposed methodology enables generating dynamically feasible local trajectory segments, which are continuous to the existing, hence provides fast local replanning for collision avoidance. The DRL agent is trained with different environmental complexities, and through the batch simulations, it is shown that the proposed methodology allows to solve fast trajectory replanning problem under given or hard dynamical constraints and provide real-time applicability for such collision avoidance applications in agile unmanned aerial vehicles. Hardware implementation tests of the algorithm with the agile trajectory tracker to a small UAV can bee seen in the following video link: https://youtu.be/8IiLQFQ3V0E .

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning


 
 
 Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates. As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.
 
 

A dual‐band high‐gain EBG resonator antenna fed by dipole in CMOS process

AbstractA high‐gain dual‐band electromagnetic band‐gap (EBG) resonator antenna (ERA) which is suitable for 60 GHz short‐range communication and 77 GHz vehicle collision avoidance application is proposed. The EBG is constructed by two identical slabs over a fully reflective ground plane. A dipole in Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm process is employed to serve as a feeder of the resonator. By combining the frequency resonances of the EBG and the feeding chip fabricated on the LTCC substrate, the proposed ERA obtains dual‐band performance. Furthermore, the proposed ERA achieves a high gain by designing two slabs to have phase shifts introduced by multiple reflections of the wave between the ground and slabs at double certain frequency points. Finally, the prototype of the antenna is simulated by High Frequency Structure Simulator (HFSS) and good agreement was achieved between simulated and measured data. The results demonstrate that it has a 10‐dB return loss bandwidth of 57.7 to 62.1 GHz and 75.2 to 77.4 GHz. Radiation efficiencies are 27.5% and 60% with 8.3 dBi gain and 14.4 dBi gain at 60 and 77 GHz, respectively.

A Novel Cross-Layer V2V Architecture for Direction-Aware Cooperative Collision Avoidance

The death toll due to highway crashes is increasing at an alarming rate across the globe. Vehicular Ad Hoc Networks (VANETs) have emerged as a promising solution to prevent crashes by enabling collision avoidance applications. However, a robust and stable collision avoidance application is a cross-layer problem that must address a number of key challenges across all layers of a VANET communication architecture. This paper presents and evaluates a novel VANET protocol suite, named Direction-Aware Vehicular Collision Avoidance (DVCA), which covers application, security services, network, and link layers. DVCA is a vehicle-to-vehicle communication architecture that provides enhanced collision probability computation and adaptive preventive measures for cooperative collision avoidance on bi-directional highways. Moreover, DVCA enables secure, in-time, and reliable dissemination of warning messages, which provides adequate time for vehicles to prevent collisions. Simulation and analytical results demonstrate reasonable reduction in collisions by DVCA, as compared with eminent VANET communication architectures.

Set-based collision avoidance applications to robotic systems

A robotic system can consist of a single or multiple agents with a fixed or mobile base, with full or under-actuation, and possibly redundancy. Collision avoidance is a crucial task for any robotic system and is necessary to ensure safe operation. In this paper, we use a set-based approach to ensure collision avoidance as a high-priority task of a robotic system while simultaneously defining one or more tasks for the system to achieve. The set-based approach is highly generic and flexible, and we present theoretical results, practical implementation and experimental results of the approach applied to a redundant, fully actuated robot manipulator, a full-scale underactuated surface vessel and a multi-agent system of unicycles.

Enhancing Relative Positioning Accuracy in Vehicle-to-Vehicle Safety: Multivehicle Kinematics and Nonlinear Particle Filtering Approach

Unsatisfactory relative positioning accuracy is a bottleneck in collision avoidance applications of vehicle-to-vehicle communication. Previous efforts focused mainly on either enhancing absolute GPS data precision or experimenting with positioning techniques alternative to GPS, such as real-time kinematics, Doppler measurements, or integration of GPS with ultrawideband ranging radios. This paper proposes a new approach that is independent from the sources and precisions of raw data. Based on a multivehicle kinematics model adapted from a multirobot domain, the method can predict vehicles’ distances and bearings by fully utilizing the shared real-time motion data from dedicated short-range communications’ basic safety message packets. Three types of nonlinear filters were proposed and compared to minimize the errors caused by raw data noise and system timing inaccuracies. Experiments indicated that the extended Kalman filter failed to estimate a highly nonlinear process, unscented Kalman filter dysfunctions, if the statistical features of errors were misestimated; only the particle filter maintained satisfactory and stable accuracies in both high-nonlinearity and error-unknown situations. The combination of the kinematics model and particle filtering can serve as an algorithmic complement to various approaches focused on data sources to enhance relative positioning precision.

Designing fail-safe and traffic efficient 802.11p-based rear-end collision avoidance

An essential, but nevertheless often neglected, objective for the design of safety-critical IEEE 802.11p-based application is: fail-safety. A fail-safe application is an application that incorporates features that automatically counteract the effect of anticipated sources of failure. In the context of a rear-end collision avoidance application two main possible sources of failure exist: an unpredictable human behavior and unreliable communication. This paper presents mechanisms that, when integrated into the design of rear-end collision avoidance application, counteract these failure cases and thus ensure fail-safety. However, fail-safety comes at a cost: either large inter-vehicle distances have to be kept to ensure that all drivers have enough time to react or the application has to take over vehicle control to allow smaller inter-vehicle distances and thus higher traffic efficiency. In this paper we analyze this tradeoff and quantify what part of drivers’ population has to be deprived of vehicle control in order to achieve acceptable traffic efficiency when deploying IEEE 802.11p-based rear-end collision avoidance application.

SymPLFIED: Symbolic Program-Level Fault Injection and Error Detection Framework

This paper introduces SymPLFIED, a program-level framework that allows specification of arbitrary error detectors and the verification of their efficacy against hardware errors. SymPLFIED comprehensively enumerates all transient hardware errors in registers, memory, and computation (expressed symbolically as value errors) that potentially evade detection and cause program failure. The framework uses symbolic execution to abstract the state of erroneous values in the program and model checking to comprehensively find all errors that evade detection. We demonstrate the use of SymPLFIED on a widely deployed aircraft collision avoidance application, tcas. Our results show that the SymPLFIED framework can be used to uncover hard-to-detect catastrophic cases caused by transient errors in programs that may not be exposed by random fault injection-based validation. Further, the errors exposed by the framework help us formulate a set of error detectors for the application to avoid the catastrophic case and other incorrect outcomes.

LIMERIC: A Linear Adaptive Message Rate Algorithm for DSRC Congestion Control

Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g., vehicle speed and location or traffic signal state and timing). Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology. This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver. This paper also extends the convergence analysis for two important cases, i.e., measurement noise in the input signal and delay in the update process. This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios.

A stochastic model for design and evaluation of chain collision avoidance applications

Cooperative/Chain Collision Avoidance (CCA) is an important type of safety-related applications of vehicular ad hoc networks (VANETs). They provide collaborative communication capabilities to vehicles in order to reduce the number of accidents on the road. Simulation is the usual choice to evaluate these systems. However, current simulation tools based on car-following models cannot be seamlessly used to simulate accidents, as we discuss here. Therefore, in this paper we propose the use of a stochastic model as an alternative to simulation for the design and performance evaluation of such applications. The model enables the computation of the average number of collisions that occurs in a platoon of vehicles, the probabilities of the different ways in which the collisions may take place, as well as other statistics of interest. The suitability of the model for evaluating CCA applications is shown by comparing our results with other authors’ simulation results. Additionally, it can be used at an early stage to shed relevant guidelines for the design of CCA applications, by disclosing the influence of kinematic parameters on the collision process. To exemplify it, we provide an evaluation of different types of CCA applications in two scenarios, a freeway and an urban scenario.

A Stochastic Model for Chain Collisions of Vehicles Equipped With Vehicular Communications

Improvement of traffic safety by cooperative vehicular applications is one of the most promising benefits of vehicular ad hoc networks (VANETs). However, to properly develop such applications, the influence of different driving parameters on the event of vehicle collision must be assessed at an early design stage. In this paper, we derive a stochastic model for the number of accidents in a platoon of vehicles equipped with a warning collision notification system, which is able to inform all the vehicles about an emergency event. In fact, the assumption of communications being used is key to simplify the derivation of a stochastic model. The model enables the computation of the average number of collisions that occur in the platoon, the probabilities of the different ways in which the collisions may take place, as well as other statistics of interest. Although an exponential distribution has been used for the traffic density, it is also valid for different probability distributions for traffic densities, as well as for other significant parameters of the model. Moreover, the actual communication system employed is independent of the model since it is abstracted by a message delay variable, which allows it to be used to evaluate different communication technologies. We validate the proposed model with Monte Carlo simulations. With this model, one can quickly evaluate numerically the influence of different model parameters (vehicle density, velocities, decelerations, and delays) on the collision process and draw conclusions that shed relevant guidelines for the design of vehicular communication systems, as well as chain collision avoidance applications. Illustrative examples of application are provided, although a systematic characterization and evaluation of different scenarios is left as future work.

계단 주파수 변조된 펄스 도플러 기법을 이용한 고해상도 전방 충돌 회피용 차량 레이다 성능 분석

A forward-looking automotive radar typically utilizes the frequency modulated continuous wave(FMCW) or pulsed-Doppler waveform for the Information acquisition of the target range and velocity. In order to obtain the high resolution target information, however, a narrow pulse width and wide bandwidth are inherently required, thus resulting in high peak power and high speed digital converter processing. In this paper, a stepped-frequency pulsed-Doppler(SFPD) waveform algorithm is proposed for high resolution forward looking automotive radar application. The performance of the proposed SFPD waveform technique is analyzed and compared with the conventional FMCW and PD method. Since this technique can be used for the high resolution target imaging with arbitrary range and Doppler resolution, it is expected to be useful In automotive radar target classification for the precision collision avoidance applications in the future.

Impacts of Radio Access Protocols on Cooperative Vehicle Collision Avoidance in Urban Traffic Intersections

This paper p resents the design of a Cooperative Collision Avoidance application, and evaluates its performance with DSRC-recommended 802.11 Medium Access Control (MAC) and with a novel Vehicular Self-Organizing MAC (VeSOMAC) protocol. VeSOMAC is designed as a fully distributed TDMA protocol that relies on an in-band control exchange technique for autonomous TDMA slot allocation among vehicle-mounted wireless communication modules. A hybrid traffic and wireless network simulator has been developed for evaluating both network level and application level performance in the presence of different wireless access protocols. Detailed network and vehicular traffic simulation models have also been developed for evaluating a Cooperative Collision Control (CCA) application, operating in urban traffic intersection scenarios. Simulation results demonstrate that unlike the 802.11 style contention based protocols, a schedule based protocol such as VeSOMAC can offer better vehicle safety performance through smaller and bounded packet latency in vehicular ad hoc networks.