Panagiotis Lytrivis

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.

Detection of maneuvers using evidence theory

The early detection of maneuvers performed by the driver is one of the most important tasks of a driver assistance system for accurately perceiving the road situation and thus warning the driver in time or intervening in the vehicle control to avoid the danger. By recognizing relationships between entities of the road environment the system can react with more efficiency to the current situation. This paper focuses on the investigation of methods regarding the identification of the maneuvering type not only for the host vehicle but also for the detected objects which are moving in the surrounding environment using the theory of evidence.

Cooperative Path Prediction in Vehicular Environments

The prediction of the future path of the ego vehicle and of other vehicles in the road environment is very important for safety applications, especially for collision avoidance systems. Todays available advanced driver assistance systems are mainly based on sensors that are installed in the vehicle. Due to the evolution of wireless networks the current trend is to exploit the cooperation among vehicles to enhance road safety. In this paper a cooperative path prediction algorithm is presented. This algorithm gathers position, velocity and yaw rate measurements from all vehicles in order to calculate the future paths. A specific care is taken for the manipulation of the latency of the wireless vehicular network. Also map data concerning the road geometry are used to enhance the estimation of path prediction. This work shows both the advances of using communications among road users and the corresponding challenges.

Multiple hypothesis tracking for automated vehicle perception

The use of multiple hypothesis tracking has proven to provide significant performance benefits over the single hypothesis GNN or the PDA algorithm. Automotive sensors like radars, laser-scanners or vision systems are being integrated into vehicles for commercial or scientific purposes, in increasing numbers over the last years. As a result, there is profound literature on this area and several approaches have been proposed to the problem of multi-target, multi-sensor target tracking. The most advanced vehicle applications allow the use of highly or even fully automated driving. Of course, these applications require an accurate, robust and reliable perception output so that the vehicle can be driven autonomously. The HAVEit EU project investigates the application and validation of automated vehicles applications, technologies that are going to have great impact in transport safety and comfort. In this paper the MHT algorithm is applied to real sensor data, installed in Volvo Technology vehicle demonstrating Automated Queue Assistance. In conjunction with simulated scenarios, the benefits in tracking performance compared to conventional GNN tracking are presented.

Multiple hypothesis tracking for data association in vehicular networks

The introduction of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications in Intelligent Transportation Systems of the future brings new opportunities and new challenges into the automotive scene. Vehicular communications broaden the information spectrum that is available to each vehicle, allowing the enhancement of existing applications and the introduction of new ones. Undoubtedly, the impact of this new technology in transportation safety, efficiency and infotainment is expected to be very important. A significant part of research in vehicular networks (VANETs) is dedicated to networking issues like routing and safety. However, perception systems which until now were based on onboard sensors only, need to incorporate the wirelessly received information in order to extend the situation awareness of the vehicle and the driver. This paper presents an algorithm for associating targets tracked from an onboard radar sensor with the position and motion data received from the VANET. The core of the algorithm is a track oriented multiple hypothesis tracker that is modified for incorporating information included in VANET messages. The algorithm is tested in real scenarios using two experimental vehicles and then compared with two other algorithmic approaches. One is using a simpler single hypothesis algorithm for association of VANET messages and the second is using only the onboard sensors for environment perception. As a result, the advantages of the Multiple Hypothesis Algorithm regarding association performance and the added value of wireless information in the perception system are highlighted.

Multiple Hypothesis Tracking Implementation

Tracking algorithm takes as input a list of clustered laserscanner measurements (objects), which for simplicity will be called measurements. The tracking algorithm forms a track whenever there is “enough” evidence that a sequence of measurements represents a real target. Additionally, by using the appropriate filtering techniques the tracking algorithm estimates the kinematic state of the formed track.

Situation refinement for in-vehicle platforms in vehicular networks

The advanced situation awareness of the driver in the road environment is very important for vehicular safety applications, especially for mitigating the harmful effects of collisions. Nowadays, advanced driver assistance systems are based on on-board sensors. The evolution of wireless networks lead to the exploitation of cooperation among vehicles to enhance road safety. In this paper the benefits from using wireless communications for enhancing the situation awareness of the driver are highlighted. The situation around the vehicle is analyzed based on information coming from both on-board sensors and wireless LAN messages. Every equipped vehicle broadcasts twice per second a message including information about its global position (WGS84), velocity, acceleration, heading, yaw rate and other useful parameters. This information in combination with advanced digital maps is very useful for enhancing safety in road environments. The work described here is part of the work carried out for the European integrated research project SAFESPOT.

Sensor Data Fusion in Automotive Applications

Sensor data fusion plays an important role in current and future vehicular active safety systems. The development of new advanced sensors is not sufficient enough without the utilisation of enhanced signal processing techniques such as the data fusion methods. A stand alone sensor cannot overcome certain physical limitations as for example the limited range and the field of view. Therefore combining information coming from different sensors broadens the area around the vehicle covered by sensors and increases the reliability of the whole system in case of sensor failure. In general, data fusion is not something innovative in research; a lot has been done for military applications, but it is rather a new approach in the automotive field. The state-ofthe-art in the automotive field is the fusion of many heterogeneous onboard sensors, e.g. radars, laserscanners, cameras, GPS devices and inertial sensors, and the use of map data coming from digital map databases. A functional model very similar to the Joint Directors of Laboratories (JDL), which is the most prevalent in data fusion, is used in automotive fusion. According to this model the data processing is divided to the following levels: signal, object, situation and application. All these levels communicate and exchange data through a storage and system manager. The JDL model is only a functional model which allows different architectures for fusion implementation. These architectures are divided in centralized, distributed and hybrid; each one has advantages and disadvantages. In the data fusion process the main focus is on object and situation refinement levels, which refer to the state estimation of objects and the relations among them, correspondingly. The discrimination between these levels is also made by using the terms low and high level fusion instead of object and situation refinement. There are several vehicular applications that fusion of data coming from many different sensors is necessary. These can be divided into three main categories: longitudinal support, lateral support and intersection safety applications. There is a current tendency to exploit also wireless communications in vehicles. Talking cars forming ad hoc networks may be useful in future applications to cover more safety cases that can not be covered so far, due to physical limitations of onboard sensors. In this way the electronic horizon and the awareness of the driver can be extended even to some kilometres away. A lot of ongoing research is focused on the design of efficient protocols and architectures for vehicular ad hoc networks and on the standardization of this kind of vehicular communication. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

On the track-to-track association problem in road environments

Multi-sensor systems in automotive safety applications and sensor data fusion have become very popular in recent years. Sensors on board cars and active safety applications are increasing in number and the need to define a common method for object extraction and serving these applications has been recognized. Authors propose a high level fusion approach suitable for automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper this track level data fusion approach is introduced with the main focus to be on the data association of tracks coming from the on board sensors with distributed processing. The core of the proposed approach is the formulation of the data association problem in presence of multipoint objects and then the solution for (a) multidimensional assignment and (b) all around vehicle object maintenance. The motivation of this work is the research work that is carried out in the project PReVENT/ProFusion2 where the proposed algorithm is being tested in two experimental vehicles.

Public attitudes towards autonomous mini buses operating in real conditions in a Hellenic city

Public attitudes are a key factor shaping the demand and market for autonomous vehicles. Previous studies are limited to a priori attitudes since such vehicles are not yet fully accessible to the public. A survey among 200 passengers of autonomous mini buses in real operation in the city of Trikala shows that they were well accepted and integrated in the peoples everyday life and that there were no major concerns as regards safety and security on board. A survey involving 519 adult Trikala citizens revealed that regular users of the autonomous mini bus were younger people, experienced with driving automation systems, who would prefer fully autonomous than manual driving and who would use an autonomous vehicle. Although the findings should be verified with bigger samples and in more cities and traffic environments, they are encouraging towards the introduction of autonomous buses in urban environments.