Angelos Amditis

Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems

Path prediction is the only way that an active safety system can predict a drivers intention. In this paper, a model-based description of the traffic environment is presented - both vehicles and infrastructure - in order to provide, in real time, sufficient information for an accurate prediction of the ego-vehicles path. The proposed approach is a hierarchical-structured algorithm that fuses traffic environment data with car dynamics in order to accurately predict the trajectory of the ego-vehicle, allowing the active safety system to inform, warn the driver, or intervene when critical situations occur. The algorithms are tested with real data, under normal conditions, for collision warning (CW) and vision-enhancement applications. The results clearly show that this approach allows a dynamic situation and threat assessment and can enhance the capabilities of adaptive cruise control and CW functions by reducing the false alarm rate.

A Situation-Adaptive Lane-Keeping Support System: Overview of the SAFELANE Approach

Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.

A Holistic Approach to the Integration of Safety Applications: The INSAFES Subproject Within the European Framework Programme 6 Integrating Project PReVENT

This paper deals with the integration of multiple advanced driver-assistance systems (ADAS) and in-vehicle information systems (IVIS) in a holistic driver-support system. The paper presents the results of a project named Integrated Safety Systems (INSAFES), which was part of PReVENT: an integrating project carried out under the European Framework Programme 6. Integration in INSAFES is tackled at three different levels in the framework of a “cognitive car” perspective: 1) at the perception level, to represent the world around the vehicle, including object-tracking between sensor fields and the detection of driver intentions; 2) at the decision level, to reproduce humanlike holistic motion plans, which serve as “reference maneuvers” to evaluate the motion alternatives that a driver faces; and 3) at the level of interaction with the driver and vehicle control ( action level), to arbitrate between the requests of functions competing for driver attention. A function that provides simultaneous longitudinal and lateral support has been developed. It gives support for safe speed, safe distance, lane change, and all-around collision avoidance all at the same time. At its core, there is a tool (evasive/reference maneuver) that constantly evaluates two possible alternatives (in lane and evasive/lane change) and compares them with the driver input to detect which one applies, which dictates warnings and driver interactions, and whether there is a better alternative. In addition, a “warning manager” has been developed, acting like a referee who lets the ADAS applications work standalone and then combines the requests of each application, prioritizes them, and manages the interaction with the user. The warning manager can be particularly useful in the case of integration of pre-existing standalone functions, which can be quickly reused. If a holistic ADAS is developed, the warning manager can still be used to combine it with IVIS functions. In fact, depending on the kind of ADAS and IVIS considered, the most suitable approach can be either to combine functions in a unified multifunctional driver-support application or to arbitrate between them through the warning manager.

Driver–Vehicle–Environment Monitoring for On-board Driver Support Systems: Lessons Learned From Design and Implementation

This paper is presenting the efforts to implement in real time and for on-board applications a set of Driver-Vehicle-Environment (DVE) monitoring modules based on the theoretical work done in DVE modelling within the EC 6th FW co funded AIDE Integrated Project. First the need for such an implementation will be discussed. Then the basic DVE modelling principles will be introduced and analysed. Based on that and on the overview of the theoretical work performed around the DVE modelling, the real time DVE monitoring modules developed in this project will be presented and analysed. To do this the DVE parameters needed to allow the required functionalities will be discussed and analysed. Special attention will be given to the use cases and scenarios of use for the real time DVE modules. This allows the reader to understand the functionalities that these modules enable in tomorrows vehicles that will integrate a large degree of automation supported by advanced integrated and adaptive human machine interfaces (HMIs). The paper will also present examples of the functional and technical tests and validation results for some of the DVE modules. The paper will conclude with a discussion around the lessons learned about the design and implementation of such systems. This will include also the next steps and open issues for research in order for these systems to become standard modules in tomorrows vehicles.

Towards the Automotive HMI of the Future: Overview of the AIDE-Integrated Project Results

The Adaptive Integrated Driver-vehicle interfacE (AIDE) is an integrated project funded by the European Commission in the Sixth Framework Programme. The project, which involves 31 partners from the European automotive industry and academia, deals with behavioral and technical issues related to automotive human-machine interface (HMI) design, with a particular focus on integration and adaptation. The project involves tightly integrated empirical research, driver-behavior modeling, and methodological and technological development. This paper provides an overview of the AIDE Sub-Project 3 results dealing with the design, development, and integration of the AIDE system in three prototype vehicles, together with the evaluation results of the trials.

An Advanced Cooperative Path Prediction Algorithm for Safety Applications in Vehicular Networks

Vehicular ad hoc networks (VANETs) are in the heart of current and future automotive research. Most of the current vehicular safety applications are based on sensors installed on the vehicle, e.g., radars and laserscanners. Due to the evolution of wireless networks, there is a tendency to exploit the cooperation among vehicles to enhance road safety through the related applications. Path prediction of a drivers own vehicle and other vehicles is crucial for road safety. Path prediction can assist the driver in having an enhanced perception of the road environment and of the intention of other neighboring drivers. In this paper, an advanced cooperative path prediction algorithm is presented. This algorithm gathers position, velocity, acceleration, heading, and yaw rate measurements from all connected vehicles to calculate their future paths. In addition, map data with regard to the road geometry and, in particularly, the road curvature are used to enhance the path prediction algorithm. Comparative results of the path prediction, with and without wireless communications, are discussed. In addition, the algorithm is adapted for use in the emergency-electronic-brake-lights application. The results of this adaptation are also presented. This paper is another contribution in highlighting the advantages and, at the same time, the challenges of using communications among road users.

Using digital maps to enhance lane keeping support systems

The development of a system that can be used for a safe, reliable, highly available onboard lane keeping support system is a critical research topic. One of the most important functions in driver assistant systems is the detection of unintentional lane departures. Current lane departure warning systems focus mainly in the detection of lane markings using vision sensors, such as CMOS cameras. In order to increase accuracy and robustness of such systems the utilization of digital maps is necessary. The goal of combining camera and map data is to extend the road geometry in further distances and eliminate false alarms based on unintentional maneuvers caused by the driver. The overall system efficiency is increased furthermore by using also vehicle dynamics and road geometry calculated using radar data.

DRIVABILITY: a new concept for modelling driving performance

Various types of driver models have been proposed in the literature, such as taxonomic, functional, and motivational. Recently, the promising Michon model was extended, leading to the widely used GADGET matrix. Nevertheless, the correlation of such models to actual road accidents and their causes has so far been weak. In addition, the use of those models for predicting driver behavioural adaptation has not proven feasible. This paper introduces a new concept for modelling drivers performance, that of DRIVABILITY. DRIVABILITY is defined as a combination of permanent and temporary factors that affect a drivers performance. Furthermore, this paper proposes a DRIVABILITY index and a methodology to measure it, in order to move from over-simplistic, hierarchical modelling to a multi-dimensional sphere. The usability of the newly proposed concept is shown through its application in three different example cases, including a system monitoring driver hypovigilance, a system for driver basic training, and an elderly drivers assessment scheme.

Fusion of infrared vision and radar for estimating the lateral dynamics of obstacles

Abstract Automotive forward collision warning systems are based on range finders to detect the obstacles ahead and warn or intervene when a dangerous situation occur. However, the radar information by itself is not adequate to predict the future path of vehicles in collision avoidance systems due to the poor estimation of their lateral attribute. In order to face this problem, this paper proposes the utilization of a new Kalman based filter, whose measurement space includes data from a radar and a vision system. Given the superiority of vision systems in estimating azimuth and lateral velocity, the filter proves to be robust in vehicle maneuvers and curves. Results from simulated and real data are presented, providing comparative results with stand alone tracking systems and the cross-covariance technique in multisensor architectures.

Revisiting JDL model for automotive safety applications: the PF2 functional model

The question raised in this paper, for the first time, is how the JDL model can be applied in multi-sensor automotive safety systems, since new sensors are integrated on-board, while new functions support the driver, intervene and control the vehicle. The paper proposes a hybrid hierarchical structure and develops a suitable functional model, namely the ProFusion2 (PF2) model; PF2 serves the broad automotive sensor data fusion community as a conceptual framework of common understanding and it provides recommendations and guidelines for implementation of fusion systems. Reference implementations are given as complete examples from the major automotive research initiative in Europe (PReVENTproject)