Azim Eskandarian

Challenges of intervehicle ad hoc networks

Intervehicle communication (IVC) networks, a subclass of mobile ad hoc networks (MANETs), have no fixed infrastructure and instead rely on the nodes themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, IVC networks exhibit characteristics that are dramatically different from many generic MANETs. This paper elicits these differences through simulations and mathematical models and then explores the impact of the differences on the IVC communication architecture, including important security implications.

A Reliable Link-Layer Protocol for Robust and Scalable Intervehicle Communications

Current link-layer protocols for safety-related intervehicle communication (IVC) networks suffer from significant scalability and security challenges. Carrier sense multiple-access approaches produce excessive transmission collisions at high vehicle densities and are vulnerable to a variety of denial of service (DoS) attacks. Explicit time slot allocation approaches tend to be limited by the need for a fixed infrastructure, a high number of control messages, or poor bandwidth utilization, particularly in low-density traffic. This paper presents a novel adaptation of the explicit time slot allocation protocols for IVC networks. The protocol adaptive space-division multiplexing (ASDM) requires no control messages, provides protection against a range of DoS attacks, significantly improves bandwidth utilization, and automatically adjusts the time slot allocation in response to changes in vehicle densities. This paper demonstrates the need for and the effectiveness of this new protocol. The exposures of the current proposals to attacks on availability and integrity, as well as the improvements effected by ASDM, are analytically evaluated. Furthermore, through simulation studies, ASDMs ability to provide message delivery guarantees is contrasted with the inability of the current IVC proposals to do the same

Mobility management in IVC networks

Inter-vehicle communication, a central component of future in-vehicle Intelligent Transportation Systems (ITS), will require the development of distributed coordination functions that operate without a fixed communications infrastructure. These functions will be critical for coordination of access to the media, message routing, and security. The stable clustering of nearby nodes is the key for the creation of a scalable network architecture. However, existing clustering algorithms for generic networks do not perform well in a vehicular, environment due to the high speeds and the constraints on vehicle mobility. This paper presents a clustering algorithm that greatly improves cluster stability.

Handbook of Intelligent Vehicles

Traffic Statistics and Challenges.- Introduction of Relevant Vehicular Systems and Control Functions.- Overview of Intelligent Vehicle Systems and Approaches.- Sensing and Situational Awareness.- Sensory Requirements: External, Internal, Positioning and Condition Monitoring.- Driver Assistance.- Trip Planning, Navigation, and Trajectory Control.- Advanced Control and Decision Systems.- Safety and Comfort Systems.- Drowsy and Fatigue Driver Detection, Monitoring, and Warning.- Computer Vision Systems and Algorithms.- Vehicular Communications Systems.- Drive-By-Wire.- Fully Autonomous Driving.- A Look to the Future.

UNOBTRUSIVE DROWSINESS DETECTION BY NEURAL NETWORK LEARNING OF DRIVER STEERING

Abstract The purpose of this study is to detect drowsiness in drivers unobtrusively to prevent accidents and to improve safety on the highways. A method for detecting drowsiness/sleepiness in drivers is developed. This method is based on an artificial neural network (ANN). Steering angle signals are preprocessed and presented to the ANN which classifies them into drowsy and non-drowsy driving intervals. The method presented here relies on signals from the vehicle steering only (steering angle) and thus presents no obstruction to the driver. A feedforward ANN was trained using an error back-propagation algorithm and tested. The training and testing data were obtained from a previous experiment in a driving simulator driven by 12 drivers, each under different levels of sleep deprivation. The network classifies driving intervals into drowsy and non-drowsy intervals with high accuracy.

Ballistic impact simulation of GT model vehicle door using finite element method

Abstract Penetration performance of GT model military vehicle door subjected to the ballistic impact of a bullet with semispherical nose shape is investigated using 3-D nonlinear dynamic explicit finite element code LS-DYNA. Finite element simulations of the door for the bullet impact velocities of 500, 1000 and 1500 m/s are carried out using plastic kinematic and Johnson–Cook material models that can characterize strain and strain rate hardening, thermal softening effects and fracture at high velocity impacts. To reduce the computational cost of the bullet-door impact analysis, only a part of the door subjected to the impact of the bullet is considered. The part of the door is idealized as a single layer circular thin plate. Finite element analysis of the single layer plate of 2 mm thickness showed full penetration of the bullet. Analysis of the plate with existing layer backed by another layer of higher thickness prevented complete penetration. Simulations with both material models also indicated a noticeable difference in the deformation of the plate and particularly the bullet upon impact indicating the thermal softening effect.

Examining the physical foundation of continuum theories from the viewpoint of phonon dispersion relation

An overview of crystal types and their interatomic force models are given and the basic feature of the dynamics of atoms in crystal is introduced. Elastic waves described by classical continuum theory, phonon dispersion relations by micromorphic theory, micropolar theory, couple stress theories and nonlocal theory are calculated and presented. The physical foundation and the applicability are examined from the viewpoint of phonon dispersion relation. Two physical examples, including the apparent change of material constants at different length scales and the macroscopic phenomenon of piezoelectricity are discussed.

Computer modeling and validation of a hybrid III dummy for crashworthiness simulation

A finite element model of the Hybrid III crash test dummy is developed for computer crash simulations. A description of the major components of the Hybrid III dummy and their finite element representations are given. The results of testing procedures required by the Code of Federal Regulations on the physical dummy are also presented and compared with results obtained from the computer model. The reasonable accuracy obtained from the model makes it useful for crashworthiness simulations when combined with other vehicle and restraint system models.

Enhancing intelligent agent collaboration for flow optimization of railroad traffic

Intelligent agents have successfully solved the train pathing problem on a small portion of railroad network [Tsen, 1995, Ph.D. Thesis, Carnegie Mellon University, USA]. As the railroad network grows, it is imperative that the agents collaborate to operate as efficiently as possible. In this paper, the authors demonstrate a collaboration protocol based on a conditional measure of agent effectiveness. Because agent effectiveness is not directly measurable, a suitable metric for agent effectiveness is introduced. Where typically agents run with uniform frequency, the collaboration protocol schedules the agents with a frequency proportional to their expected effectiveness. This protocol introduced a 10-fold improvement in the agent efficiency when tested with a simulation program on a portion of the Burlington Northern railroad.

Vehicle crash modelling using recurrent neural networks

The initial velocity and structural characteristics of any vehicle are the main factors affecting the vehicle response in case of frontal impact. Finite Element (FE) simulations are essential tools for crashworthiness analysis, however, the FE models are getting bigger, which increases the simulation time and cost. In the current research, an advanced Artificial Neural Network (ANN) was used to store the nonlinear dynamic characteristics of the vehicle structure. Therefore, several impact scenarios can be analyzed quickly with much less computational cost by using the trained networks. The equation of motion of the dynamic system was used to define the inputs and outputs of the ANN. The system dynamics was included in the network performance and the recurrent back-propagation learning rule was adapted in training the network. The results of the numerical examples indicated that the recurrent ANN can accurately capture the frontal crash characteristics of the impacting structures, and predict the crash performance of the same structures for any other crash scenario within the training limits.