Research

Current Research

I currently work on research and policy-related activities regarding the impact of Connected and Automated Vehicle (CAV) technologies on future road transport systems, in terms of Traffic Congestion, Emissions and Energy Efficiency (Fuel Consumption and Tractive Energy Consumption), through a combined approach based on traffic modelling and simulation, desktop research and stakeholders’ consultation.

Traffic modeling and simulation

Traffic Flow

Simulation experiments are conducted for various traffic mixtures of manually-driven vehicles, AVs and CAVs, different desired time headways settings and traffic demand levels, to evaluate the sensitivity of the network performance to these factors. The ring road of Antwerp is used for the case study. Thus, the results and conclusions refer to a large real-world network. The consequences of the introduction of AVS and CAVs on traffic flow and pollutant emissions are evaluated. The results show that depending on the demand, AVs introduction can have negative effects on traffic flow, while CAVs may benefit the network performance, depending on their market penetration.

Traffic Safety

Ensuring safe driving behavior is the highest priority for all stakeholders involved in the AV deployment phase, the industry, the regulatory bodies and the consumers and one of the things that AVs have to achieve in order to succeed. Considering the driver behavior, Mobileye/Intel proposes the Responsibility-Sensitive Safety (RSS) framework to form the basis of industry standards. We perform an assessment of the framework through microsimulation regarding possible effects on the traffic flow and we study the sensitivity of the model by introducing variability in its parameters’ set.

CO2 emissions

We study the evolution of emissions over the highway network for CAVs through microsimulation. Each vehicle technology generates different driving behaviour. Hence, the motivation is to answer whether the different driving behaviours produce significant differences in emissions during rush hours, and how significant is the impact of detailed vehicle dynamics simulation and instantaneous emissions in the outcome. The status of the network is assessed in terms of flow and speed. Furthermore, emissions are estimated using both the average-speed EMEP/EEA fuel consumption factors and a generic version of the European Commission’s CO2MPAS model that provides instantaneous estimations. Conservative driving of AVs can deteriorate the status of the network, and that connectivity is the key for improved traffic flow.

Vehicle Free-Flow Acceleration Dynamics, Simulation and Reality

Microscopic traffic simulation models are widely used to assess the impact of measures and technologies on the road transportation system. The assessment usually involves several measures of performance, such as overall traffic conditions, travel time, energy demand/fuel consumption, emissions, and safety. In doing so, it is usually assumed that traffic models are able to capture not only traffic dynamics but also vehicle dynamics (especially to compute energy/fuel consumption, emissions, and safety). However, this is not necessarily the case with the possibility of achieving unreliable outcomes when extrapolating from traffic to measures of performance related to the vehicle dynamics. A set of experiments was carried out in the Vehicle Emissions Laboratories of the European Commission Joint Research Centre in order to generate relevant data sets. These experiments are used to test the performance of three well-known car-following models. Although all models have been largely tested against their capability to correctly reproduce traffic dynamics, the findings raise concerns about their capability (and thus of the traffic models using them) to predict the effect on the microscopic vehicle dynamics and thus on emissions and energy/fuel consumption.

Vehicle dynamics free-flow modeling

The acceleration pattern of a vehicle plays an essential role in the estimation of the energy required during its motion, and therefore in the fuel consumption and the CO2 emissions. A lightweight microsimulation free-flow acceleration model (MFC) has been developed that is able to capture the vehicle acceleration dynamics accurately and consistently, it provides a link between the model and the driver and can be easily implemented and tested without raising the computational complexity. The proposed model is calibrated, validated, and compared with known car-following models on road data on a fixed route inside the Joint Research Centre of the European Commission. Finally, the MFC is assessed based on 0–100 km/h acceleration specifications of vehicles available in the market.

Experimental Campaigns

Response Time and Time Headway of commercially-available Adaptive Cruise Control (ACC) Systems

Road vehicles are characterized by increasing levels of automation and it is vital to understand the future impact on transport efficiency. ACC is one of the first and most common automated functionalities available in privately owned vehicles. The effect of ACC on traffic flow has been widely studied by making assumptions on its operating strategy and on some of its important parameters such as the response time and the desired time headway. In the literature, these parameters are usually set to low values, based on the vehicle controller's theoretical ability to respond within a very short time frame. A new methodology for the estimation of the controller's response time and the desired time-gap was developed to this objective. Results show that the response time of the particular ACC controller was in the range 0.8s-1.2s, which is similar to what is commonly assumed for human drivers.

String Stability of ACC Systems

An experimental campaign with 5 vehicles equipped with ACC has been organized in the proving ground of AstaZero in Sweden to raise understanding on the properties of the ACC systems and their functionality under real driving conditions. The main parameters under investigation are the response time of controllers, the available time headway settings and the stability of the car-platoon. Imposed perturbations of variable magnitudes lead to instability for the car-platoon. Furthermore, instability appears even for slight perturbations derived by the variability in the road slope. Numerical differentiation on the altitude shows a negative correlation with the speed trajectory of the leading vehicle.

Energy Demand of ACC Systems

The ACC impact on tractive energy consumption is evaluated with real-world driving data from a highway multiple-car-following campaign. The results demonstrate that consecutive ACC followers can cause string instability, however, the manual counterparts can mitigate the speed perturbations propagating upstream. Consequently, there exists a considerable energy consumption increase (3 – 21 %) regarding ACC car-following behaviours, compared with the manual counterparts. Additionally, the energy consumed by ACC followers in the same fleet tend to increase (11.2 – 17.3 %) as the speed perturbations propagate upstream.

Signal Processing - GNSS data

Generation of ego-vehicle trajectory data and relative positioning in comparison to other vehicles on the lane is a difficult task due to low accuracy of GPS positioning systems and the prohibitive cost of high-end equipment. We work testing budget system with high-accuracy differential GPS measurement to quantify their accuracy and provide signal processing methods that are capable to produce reliable vehicle position and speed trajectories of multiple agents at high frequencies.

Past Research

Data Mining

Infer of the itinerary of cargo transported in shipping containers based on a large, heterogeneous and noisy dataset of Container Status Messages. Such itinerary information can be used to improve the risk analysis performed by authorities in their effort to secure the global trade and produce reliable anti-fraud risk indicators.

Quality Assessment of Risk Indicators

We proposed a quality assessment framework that combines quantitative and qualitative domain specific metrics to evaluate the quality of large datasets of Container Trip Information records and to provide a more complete feedback on which aspects need to be revised to improve the quality of the output data.

Automatic archaeological sherd classification

Sherds that are found in the field usually have little to no visible textual information such as symbols, graphs, or marks on them. This makes manual classification an extremely difficult and time-consuming task for conservators and archaeologists. For a bunch of sherds found in the field, an expert identifies different classes and indicates at least one representative sherd for each class (training sample). We developed an image processing technique that uses local features based on color and texture information to automatically cluster similar pottery fragments and speed up the archaeologists' labelling tasks.

Jigsaw puzzle solving

This work was on providing an automatic jigsaw puzzle solution without any initial restriction about the shape of pieces, the number of neighbor pieces, etc. The proposed technique uses both curve- and color-matching similarity features and applies a recurrent algorithm that matches puzzle pieces in pairs aiming to restore the original image.

Analysis, Segmentation, Restoration of degraded document images

We worked on global and local skew detection in complex color documents with both contextual and graphical information, segmentation of historical machine-printed documents for the detection of characters, words and sentences, as well as document binarization and color reduction.