Demonstration of Forward Collision Avoidance Algorithm Based on V2V Communication

Connected vehicles transmit and receive basic safety messages that enable the use of safety applications intended to avoid crashes. The information age (IA) is the latency between the basic safety message being generated by the transmitting vehicle and received by another vehicle. The receiving vehicle’s processing time is included in the IA but, at several milliseconds, can be ignored. The IA of vehicle-to-vehicle communications needs to be kept beneath a certain threshold so as not to compromise the effectiveness of safety applications. Forward collision warning (FCW) requires a critical IA in scenarios in which a lead vehicle brakes hard and abruptly. An analytical model of FCW, with the lead vehicle coming to a complete stop, is proposed. For the host vehicle behind the lead vehicle to stop safely, an appropriate IA, perception–reaction time, and deceleration rate are required. Sensitivity analysis of the IA is performed to provide the probabilities of a safe stop for various combinations of speed and deceleration rate. Monte Carlo simulations are implemented to generate the probabilities, and perception–reaction times, speeds, and deceleration rates are formulated as probability density functions. The results show that the IA is a sensitive factor in the safe operation of an FCW application. The IA for a low-speed host vehicle is more sensitive than the IA for a high-speed host, when the host and lead vehicles are both traveling at similar speeds with a normal clearance gap. The IA is more sensitive when the host vehicle speed is low (≤40 mph) or when the lead vehicle’s deceleration rate is high (≥0.5 g).

Dedicated Short Range Communication (DSRC) technology has the capability to dramatically improve safety in transportation not only for transit vehicle passengers as well as pedestrians. Connected Vehicles (CVs) utilize onboard units (OBUs) to communicate with other vehicles and/or infrastructure with Basic Safety Messages (BSMs). With such communication, the host vehicle (HV) can accurately determine the position, velocity, and heading direction of nearby remote vehicles (RV s). There exist many hazardous vehicle-to-vehicle (V2V) scenarios such as Blind Spot Warning (BSW) Front Collision Warning (FCW), Intersection Movement Assist (IMA), Lane Change Assist (LCA), etc. With inexpensive embedded onboard units in terms of power consumption, data generation, and financial cost in general, it is possible to overcome constraints presented by vehicles relying on sensors and radars e.g. poor visibility due to weather and objects. By extending previous work on the determination of relative angle with the RV, an innovative approach is proposed to address two V2V applications, IMA and LCA utilizing only BSMs. Furthermore, the aforementioned approach, along with different scenarios, is implemented and simulated using the CARLA simulator.

Driver assistance system in most of the present vehicle was using preemptive scheduler based operating system for adaptive cruise control function. Highly predictable and at low cost system can be implemented using Time triggered cooperative scheduler, instead of a full RTOS, RTOS require more calculation time and large memory space which is not available in smaller embedded systems and is very complex and difficult to debug errors. One of the simplest methods is Time Triggered cooperative scheduler for controlling adaptive cruise control in vehicle and will be most reliable and predictable. The proposed system is TTC scheduler for Adaptive cruise control, is main control parts in driver assistant system. The controller is placed in host vehicle and minimum distance between host and front vehicle is maintained by using distance sensor for distance of front vehicle, speed sensor for getting speed of host vehicle and position sensor for getting the position of accelerator pedal. The controller vary the speed of the host vehicle so that to keep minimum distance. The system is simulated using keil IDE tool. The application tasks are scheduled by using efficient Time triggered cooperative scheduler. The scheduler is written in Embedded ‘c’ and Hardware consists of ARM 7 based microcontroller LPC2148.

This research paper is devoted for discussing a Vision-Based Collision Avoidance System that aims to provide the driver with a "third eye" to help him/her to detect obstacles and estimate distance between them and the host vehicles. It is based on a monocular approach of image processing that has one camera, which continuously captures images of the frontal view of the vehicle. Then the captured images are processed in order to detect obstacles, then estimate their distances from the host vehicle and, finally, take decisions to avoid them. The detection algorithm depends on detecting the shadow of the obstacles, as an invariant feature for all types of obstacles. Watershed segmentation technique is used to detect objects and triangulation technique is used to calculate the distance between the host vehicle and the detected obstacle. The proposed system can automatically control electric vehicles.

This paper presents a novel method for locally estimating vehicle density on highways based on vehicle-to-vehicle (V2V) communication, a communication mode within intelligent transport systems (ITSs), enabled via IEEE 802.11p and 3GPP C-V2X technologies. Awareness messages (AMs), such as basic safety messages (BSMs, SAE J2735) and cooperative awareness messages (CAMs, ETSI EN 302 637-2), are periodically broadcast by vehicles and can be leveraged to sense the presence of nearby vehicles. Unlike existing approaches that directly combine the number of sensed vehicles with measured packet reception ratio (PRR) of the AM, our method accounts for the deviations in PRR caused by imperfect channel conditions. To address this, we estimate the actual packet reception probability (PRP)–distance curve by exploiting its inherent downward trend along with multiple measured PRR points. From this curve, two metrics are introduced: node awareness probability (NAP) and average awareness ratio (AAR), the latter representing the ratio of sensed vehicles to the total number of vehicles. The real density is then estimated using the number of sensed vehicles and AAR, mitigating the underestimation issues common in V2V-based methods. Simulation results across densities ranging from 0.02 vehs/m to 0.28 vehs/m demonstrate that our method improves estimation accuracy by up to 37% at an actual density of 0.28 vehs/m, compared with methods relying solely on received AMs, without introducing additional communication overhead. Additionally, we demonstrate a practical application where the basic safety message (BSM) transmission rate is dynamically adjusted based on the estimated density, thereby improving traffic management efficiency.

In this paper, we propose a transportation safety algorithm based on vehicle to vehicle communication (V2V) and explain its implementation. The proposed algorithm benefit from the information that a host vehicle can get from target vehicle to calculate the time to collision (TTC) and put thresholds for this calculated parameter to make two level collision avoidance, warning and full break according to predefined transportation scenarios. The traffic scenarios of interest in this research is the Rear-end/Forward Collision Avoidance (FCA) and Intersection Collision Avoidance (ICA). The experimental work are implemented and simulated by using the combined tools `Prescan'[1] and `MATLAB'. The main contribution in this work is to avoid the collision by adapting the vehicle's velocity not by just stopping it. The proposed algorithms are confirmed from simulation results. Contently, these algorithms can be used as a general method to reduce both the time and cost consumed for commercialization of safety services under the umbrella of IEEE 802.11p/wireless access in vehicular environments wireless networks (WAVE).

Basic safety message (BSM) are messages that contain core elements of a vehicle such as vehicle’s size, position, speed, acceleration and others. BSM are lightweight messages that can be regularly broadcast by the vehicles to enable a variety of applications. On the other hand, event-driven message (EDM) are messages generated at the time of occurrence such as accidents or roads sliding and can contain much more heavy elements including pictures, audio or videos. Security, architecture and communication solutions for BSM use cases have been largely documented on in the literature contrary to EDM due to several concerns such as the variant size of EDM, the appropriate architecture along with latency, privacy and security. In this paper, we propose a secure and blockchain based EDM protocol for 5G enabled vehicular edge computing. To offer scalability and latency for the proposed scenario, we adopt a 5G cellular architecture due to its projected features compared to 4G tong-term evaluation (LTE) for vehicular communications. We consider edge computing to provide local processing of EDM that can improve the response time of public agencies (ambulances or rescue teams) that may intervene to the scene. We make use of lightweight multi-receiver signcryption scheme without pairing that offers low time consuming operations, security, privacy and access control. EDM records need to be kept into a distributed system which can guarantee reliability and auditability of EDM. To achieve this, we construct a private blockchain based on the edge nodes to store EDM records. The performance analysis of the proposed protocol confirms its efficiency.

In this paper, we propose the intersection collision avoidance (ICA) algorithm with V2X communications. The ICA algorithm is developed based on both basic safety message (BSM) and signal phase and timing (SPaT) message of the SAE J2735 dedicated short range communications (DSRC) message set dictionary standard. Furthermore, the developed ICA algorithm is verified by utilizing ‘PreScan’. Consequently, it is confirmed that the proposed ICA developing methodology can be used as a solution tool to reduce the cost and the time consumption required for commercialization of safety services under IEEE 802.11p/WAVE wireless networks.

Implementation of simulated Vehicle-to-Vehicle (V2V) communication results using Basic Safety Message (BSM) of 320 Bytes size for the purpose of congestion management and control is carried out. The obtained results used as input to a neural network architecture. The Neural structure uses Weigend Weight Elimination Algorithm (WWEA) to achieve learning in order to predict congestion events by predicting hops count, average network lifetime, average route length and provide them as inputs to the Congestion Management Algorithms (CMA) that correlate between them in order to detect congestion events. This will also aid road traffic designers in their effort for a less congestive roads, and work towards smart applications. Testing results showed that congestion can be determined and predicted in advance, such that a control mechanism can be initiated to re-route traffic with traffic lights timing also intelligently adjusted to alleviate expected traffic congestion and related problems.KeywordsIntelligent Transportation SystemsConnected vehiclesCongestionNetwork lifetimeV2VNeural networks

Road accidents claim over a million lives every year. Some of the causes of these accidents are poor visibility of roads, false estimation of nearby vehicles and delay of driver to hit the brake. The developed IoT-based system focuses on reduction of accidents by addressing these causes. It alerts the driver about the presence of humps and potholes on the road by detecting it in advance. The visual alerts are provided by various coloured LEDs and the audio alert is provided using voice communication. The system also measures distance between the host vehicle and the vehicle ahead to maintain a safe distance of 400 m and warns the driver if safe distance is not maintained. This feature also helps in avoiding collisions with other vehicles and unidentified objects. In the worst-case scenario, if an accident occurs, this system tracks the vehicle's geographical location and provides a message alert to the registered emergency contacts. The system is equipped with vehicle-to-vehicle communication for data transmission amongst vehicles using Li-Fi technology. The range of this V2V communication is up to 2–3 m. With this feature, the host vehicle can transmit information about emergency brake situations and presence of emergency service vehicles in the vicinity for clearing the driving path. Audio and visual mechanisms are employed for alerting the driver. The system is developed by incorporating ultrasonic sensors with Arduino for testing purposes. Every feature of the system is tested with real vehicles in simulated circumstances. The performance of the system is satisfactory in all test environments.

Basic Safety Messaging plays a crucial role to provide road safety in vehicular ad-hoc networks (VANETs). To avoid potential accidents, vehicles periodically broadcast safety information to neighboring vehicles. However, due to transmission collisions, fading channels and other factors, vehicular networks usually suffer a low packet delivery ratio (PDR) and a large delay, which are intolerant of many safety applications. To tackle these issues, this paper proposes a hybrid medium access control (MAC) protocol for basic safety message (BSM) dissemination based on the framework of Dedicated Short-Range Communication (DSRC). Its partially centralized and partially distributed characteristic not only can effectively suppress the collisions, but keep compatibility with IEEE 802.11p. In addition, the integration of Physical-Layer Network Coding (PNC) and Random Linear Network Coding (RLNC) further strengthens the reliability and efficiency for BSM dissemination. Both the theoretical analysis and comprehensive simulations indicate that, compared with existing schemes, the proposed protocol can significantly improve the PDR by a range of 20% to 300%. Meanwhile, in terms of normalized throughput, it increases by varying percent between 20% and 160% in different scenarios.

Objectives: In India, the frequency of traffic collisions is among the highest in the world. Most of the accidents are due to blind spots which cannot be directly observed by the driver at control. The paper proposes a collision avoidance system due to blind spots and in the case of heavy traffic scenarios. Methods: The proposed system avoids collision in blind spot and in heavy traffic and does not follow the path of preceding vehicle if the space between detector (Our Vehicle) and host vehicle is in proximity. For implementation of the system, Arduino boards and other drivers along with ultrasonic sensor are used and ZigBee is used for data communication between two nodes. Findings: The Blind Spot Detection (BSD) procedure based on wireless detecting technique ZigBee is proposed to monitor the blind spot region for the presence of interferences, automobiles, or other objects with the aid of ultrasonic sensor. For detecting the vehicles in blind spot, Neural Fuzzy Logic is used for the proposed system. The connection models for the developed system is identified and proposed. The paper also discusses about the advantages of ZigBee over other wireless technologies. The Wireless Technology identified is ZigBee which will lower the wiring connection in the vehicles. Applications: This method is useful to avoid collision in blind spot and also lowered the wiring connection in the vehicles by using ZigBee. Hence, the proposed system discovers its application in real time and gives collision free environment. The system is evaluated for different driving scenarios and results are discussed. Keywords: Blind Spot Detection, Collision-Avoidance System, ZigBee Pair

This paper presents a method of vehicle lateral collision avoidance based on receding horizon strategy. In this paper, the vehicles’ position and velocity information is obtained based on the millimeter-wave radar, the global satellite positioning system (GPS) and the vehicle to vehicle communication technology. Then the method of transforming the vehicles’ position and speed to the host vehicle coordinate system is given. The trajectory prediction model and collision recognition algorithm of the host vehicle and the other vehicle are established based on the kinematic model. The receding horizon control algorithm is used to control the host vehicle speed in order to avoid the coming collision. The simulation results show that the host vehicle can achieve the goal of lateral collision avoidance by receding horizon control algorithm under the condition of constant and changed vehicle speed.

Road accidents and traffic congestion are challenging issues for global transport systems. Autonomous vehicles and connected vehicles are promising technologies to improve road safety and reduce traffic congestion. In this paper we investigate the performance of connected vehicles and assess the positive impact of connected vehicles on the collaborative road safety applications. To achieve the research goal, we first develop a low cost and scalable connected vehicles testbed, which consists of laptops equipped with Wi-Fi transceivers. The main concern is that collaborative road safety applications need to be widely tested with vehicle to vehicle (V2V) communications. However, the commercial dedicated short-range communications (DSRC) devices are very expensive, which makes it difficult to set up a large connected vehicles testbed. Based on the experiments on the V2V communications, the performance of forward collision avoidance under emergency events, which is a representative collaborative road safety application, is analysed. Interesting insights to the performance of V2V and the improvement of collision avoidance with V2V are obtained. The research works could be used for the analysis of connected vehicles impact on other collaborative roads safety applications.

CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) based relay-assisted V2V scheme is effective in compensating shadowing loss and hence improves performance of ITS V2V communications, especially for non-line-of-sight (NLOS) areas such as intersections. However, when the number of vehicle stations becomes large, packet congestion happens frequently at the relay station (RS). It causes interruption of packet relaying transmission and limits the improvement by the scheme. This paper proposes a packet payload combining relay (PCRL) scheme to alleviate the congestion issue at RS by compressing packet overheads of broadcast packets. With adaptive modulation and coding technique at RS, PCRL can further mitigate the packet interruption by reducing the air time of relayed packets. Time division grouping method is also applied to PCRL to mitigate the hidden terminal problem at intersections. Analyzed and simulated results show that the proposed schemes can effectively mitigate packet congestion issue and improve the reliability of the V2V communication.