2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

With the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.

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A survey on motion prediction and risk assessment for intelligent vehicles

To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles. Multiple interacting agents, the multi-modal nature of driver behavior, and the inherent uncertainty involved in the task make motion prediction of surrounding vehicles a challenging problem. In this paper, we present an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways. Our model assigns confidence values to maneuvers being performed by vehicles and outputs a multi-modal distribution over future motion based on them. We compare our approach with the prior art for vehicle motion prediction on the publicly available NGSIM US-101 and I-80 datasets. Our results show an improvement in terms of RMS values of prediction error. We also present an ablative analysis of the components of our proposed model and analyze the predictions made by the model in complex traffic scenarios.

Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs

The growing size of cities and increasing population mobility have determined a rapid increase in the number of vehicles on the roads, which has resulted in many challenges for road traffic management authorities in relation to traffic congestion, accidents, and air pollution. Over the recent years, researchers from both industry and academia have been focusing their efforts on exploiting the advances in sensing, communication, and dynamic adaptive technologies to make the existing road traffic management systems (TMSs) more efficient to cope with the aforementioned issues in future smart cities. However, these efforts are still insufficient to build a reliable and secure TMS that can handle the foreseeable rise of population and vehicles in smart cities. In this survey, we present an up-to-date review of the different technologies used in the different phases involved in a TMS and discuss the potential use of smart cars and social media to enable fast and more accurate traffic congestion detection and mitigation. We also provide a thorough study of the security threats that may jeopardize the efficiency of the TMS and endanger drivers' lives. Furthermore, the most significant and recent European and worldwide projects dealing with traffic congestion issues are briefly discussed to highlight their contribution to the advancement of smart transportation. Finally, we discuss some open challenges and present our own vision to develop robust TMSs for future smart cities.

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A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches

Path prediction is the only way that an active safety system can predict a driver's 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-vehicle's 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.

Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems

Situation and threat assessment is considered as the highest level of abstraction in the vehicle tracking processes. In this paper, a broad discussion is introduced on algorithms for active safety functions, whilst a new dynamic algorithm is proposed. This approach handles all objects' states as dynamic stochastic variables and based on a Kalman approach calculates in real time all trajectories respectively. Thus, a reconstruction of the traffic scene can be achieved in order to assess a level of threat for all moving and stationary obstacles in the longitudinal area of the subject vehicle. This approach is adopted in the European co-funded project "EUCLIDE", which develops a vision enhancement and collision warning system merging the functionality of an infrared camera and mmw radar sensor. Results are presented using simulated and real data sets from dedicated sessions.

Dynamic situation and threat assessment for collision warning systems: the EUCLIDE approach

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.

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

The car was born around a century ago and its evolution has been incredibly fast, both in technology and in style. We have to move through different social and cultural evolutions to arrive to the present state of the art. The technical and social acceleration of the 20th century is well visible looking at the different worldwide research programs. Nowadays digital content and ubiquitous computing are changing us and our life style. New concepts involving the full society are emerging and the term “personal mobility” becomes more and more used together with “co-operative driving” and “environmental compatibility”. Human Machine Interaction (HMI), initially limited only to the primary in-vehicle commands, has been a major issue since the beginning. In which direction is it moving? Which technological efforts will be key factors to face the challenges of the future? We are in the middle of a transition phase where the world has to cope with and to solve big problems as energy and climate change that can strongly influence the future of the automotive industry and not only.

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Driver-vehicle interfaces and interaction: where are they going?

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.

Luisa Andreone

Papers

Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems

Dynamic situation and threat assessment for collision warning systems: the EUCLIDE approach

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

Driver-vehicle interfaces and interaction: where are they going?

Communication and interaction strategies in automotive adaptive interfaces