A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driver’s behaviours. However, drivers’ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and its’ emerging technologies to monitor and alert driver’s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driver’s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioural in the future

IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driver’s behaviours. However, drivers’ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and its’ emerging technologies to monitor and alert driver’s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driver’s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioural in the future

An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.Comment: 15 pages and 5 figures, Submitted to the international conference on Contemporary issues in Data Science (CiDaS 2019), Learn more about this project at https://iasbs.ac.ir/~ansari/fara

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\u27s 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

Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems

World-wide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error. The main objective of this research is to develop a predictive system for driving maneuvers by analyzing the cognitive behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle is being driven). Advanced Driving Assistance Systems (ADAS) include different driving functions, such as vehicle parking, lane departure warning, blind spot detection, and so on. While much research has been performed on developing automated co-driver systems, little attention has been paid to the fact that the driver plays an important role in driving events. Therefore, it is crucial to monitor events and factors that directly concern the driver. As a goal, we perform a quantitative and qualitative analysis of driver behavior to find its relationship with driver intentionality and driving-related actions. We have designed and developed an instrumented vehicle (RoadLAB) that is able to record several synchronized streams of data, including the surrounding environment of the driver, vehicle functions and driver cephalo-ocular behavior, such as gaze/head information. We subsequently analyze and study the behavior of several drivers to find out if there is a meaningful relation between driver behavior and the next driving maneuver

Development Of Algorithms For Vehicle Classification And Speed Estimation From Dynamic Scenes By On-Board Camera Using Image Processing Techniques

Vehicle assistance system applications benefit the drivers and passengers to promote better and safer driving situations. In terms of usability of dash camera, most vehicle owners pre­ installed the camera as a personal safety purpose to record the path they went through. The wide availability of various models of the dash cameras on the market, however, lacks in intelligence to process the information that can be obtained from the camera technology system itself. Moreover, in most studies for Intelligence Transport System (ITS), the implementation of static camera, for example CCTV, is popular thus, it is an encouragement for improvement to develop a vehicle assistance system using dynamic camera scenes. The main purpose of this research was to develop a vehicle detection, vehicle classification, and vehicle speed estimation system in dynamic scenes fully by image processing technique. The scope of this research covered Malaysia highway in Skudai, Johor; Ayer Keroh, Melaka and Kajang, Selangor. Video database of these highway areas was recorded by the on-board camera unit placed on the front dashboard area of the host vehicle. Image dataset was collected with positive image sets containing four vehicle classes namely car, lorry, bus, and motorcycle. It was decided that the technique for vehicle detection were Haar-Like and Cascade Classifier while vehicle classification was based on the ratio characteristics of the vehicle detected. The use of ratio value was an added advantage for the classification process since the prepared image dataset were based on each vehicle class dimension and the ratio value are the uniqueness property for each vehicle class. Speed estimation of the vehicle started with host vehicle speed estimation by lane detection technique since the road lane was the most consistence moving object inside the video region. The Host vehicle distance measurement used the broken lane detection and for a scale factor calculation, the width of the highway lanes was calculated by measuring the lane width inside the image and calibrated with real value in meter of the lanes stated by (Jabatan Kerja Raya, 1997). Detected vehicle speed measurements were based on its centroid tracking measurements. Result analysis on accuracy measurement in vehicle detection system obtained 0.93 true positive rates from 300 vehicles presented in the video data. Further analysis in vehicle classification was proved to obtain true positive rate of 0.98 for car class, 0.89 for lorry class, 0.89 for bus class, and 0.75 for motorcycle class. For analysis of speed estimation achieved with the average percentage 6.42% for speed error of host vehicle tested on 10 different videos. In detected vehicle, it speed estimations were based on the host vehicle speed calculation by observation its position and motion behavior in comparison with the host vehicle speed value. Overall the e development indicated that image processing has the ability to visualize the surrounding area for drivers and passengers that was near to real human visions a contribution to human-machine interactions that can be beneficial