Aris Polychronopoulos

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.

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)

Unscented Kalman filter design for curvilinear motion models suitable for automotive safety applications

Research in automotive safety leads to the conclusion that modern vehicle should utilize active and passive sensors for the recognition of the environment surrounding them. Thus, the development of tracking systems utilizing efficient state estimators is very important. In this case, problems such as moving platform carrying the sensor and maneuvering targets could introduce large errors in the state estimation and in some cases can lead to the divergence of the filter. In order to avoid sub-optimal performance, the unscented Kalman filter is chosen, while a new curvilinear model is applied which takes into account both the turn rate of the detected object and its tangential acceleration, leading to a more accurate modeling of its movement. The performance of the unscented filter using the proposed model in the case of automotive applications is proven to be superior compared to the performance of the extended and linear Kalman filter.

Communication and interaction strategies in automotive adaptive interfaces

Nowadays, drivers have to cope with a growing amount of information coming from on-board information messages, telematics and advanced driver assistance systems. The interaction between the driver and these systems is critical, since they may distract the driver from the primary task of driving. The paper, addressing this problem, aims at presenting the methodological framework for the optimization of human machine interfaces (HMI) in the automotive research area; thus, the proper communication and interaction strategies are designed, in order to deliver to the driver a message or a warning in the optimal way in terms of driver safety. The proposed methodology is adopted in the COMUNICAR project and relevant results are presented. Last but not the least, the AIDE integrated project and its vision is also proposed as the roadmap for future activities in the HMI sector.

DESIGN AND DEVELOPMENT OF AN ADAPTIVE INTEGRATED DRIVER-VEHICLE INTERFACE: OVERVIEW OF THE AIDE PROJECT

Abstract This paper presents the sub-project 3 of the AIDE (Adaptive Integrated Driver-vehicle Interface) Integrated Project; AIDE is a pan European project co-funded by the European Commission, coordinated by Volvo Technology and managed by a core group with the participation of both the industry (Bosch, CRF, PSA, and BMW) and the academia (ICCS, TNO, Joint Research Centre). The main objective of the SP3 and the paper is the design and development of an innovative adaptive integrated human-machine interface for driver assistance, information and nomad systems. To address this objective different modules are described which monitor in real time the driver, the environment and the vehicle and to which the HMI is adapted. The information data flow, the communications and the interaction is ensured by a dedicated centralized module, namely the Interaction and Communication Assistant (ICA), which is considered as the main innovation and is described in details in the paper.

Information data flow in AWAKE multi-sensor driver monitoring system

Hypovigilance detection and warning systems are currently based on stand alone sensor approaches. This paper presents a multisensor system that allows the information fusion of different sources (vehicle, driver and environmental sensing parameters) and contributes to the decrease of false alarms and misses of the hypovigilance detection system. A hybrid scheme- centralized communication and data flow management of integrated stand alone systems- is adopted, which in turn, allows the real time application to monitor the driver and provide imminent and information messages according to his/her state and adapted to the external traffic and environmental scenario. The data flow between all systems, sensors and modules is described to synthesize the functional architecture. The system development is funded by the European so-called AWAKE project.

A survey of filtering techniques for vehicle tracking by radar equipped automotive platforms

In modern automotive safety applications, the use of radar technology seems to be a promising technique. Vehicles equipped with on board radar sensors aim at detecting moving or stationary objects in the sensors field of view and identifying critical collision situations. Thus, reliable tracking is of crucial importance for efficiency improvement in such systems. The presence of non linearities in measurement space is quite common in these architectures, i.e. the existence of radial velocity measurements in absence of lateral velocity ones generates a non-linear measurement model. In this paper, filtering techniques for the solution of this problem are tested, including Kalman filter and particle filters solutions. The test set of this study is consisted of a simulated highway scenario and the efficiency of filtering in position estimation; velocity estimation and time delay is checked.

Situation refinement for vehicle maneuver identification and driver's intention prediction

In safety automotive applications the system must me capable of early recognizing the maneuvers performed by the driver and the intention associated with them in order to take preventive measures or trigger warning alarms. This is done by the situation refinement level in the fusion system that processes the data provided by the on-board sensors. By recognizing relationships between entities of the road environment the system can react with more efficiency to the current situation. For example the intention of a lane change, the detection of an overtaking maneuver, the estimation of the lane in which the detected vehicle is located, help the system to decide which action must be taken in order to prevent an unwanted situation. This paper focuses on the investigation of methods regarding the identification of the maneuvering type and the intention associated.

Prediction of unintentional lane departure using evidence theory

The scope of this paper is the development of algorithms for driving support systems for safe lane changing maneuvers (lane change assist system) and safe maintenance of the vehicles path (lane/road departure warning system). The proposed algorithm can predict unintentional lane changes before they are performed by the driver using information from multiple sources. The prediction of unintentional lane changes comes from data produced by various sensors installed on the vehicle (inertial sensors, radar and camera). The decision fusion algorithm is based on Dempster-Shafer theory. The paper analyzes vehicle kinematics, extracts distributed decision components from sensor data and investigates several set of information sources and their mass functions to be utilized in the decision fusion system. Finally, uncertainties of each source are modeled and included in the Dempster-Shafers theory as weights calculated adaptively and in real-time using heuristics and a-priori knowledge. The algorithms are validated in real world scenarios.