Nowadays air quality is a major public health issue. Transport, and especially road traffic is one of the main contributor of greenhouse gases and pollutant emissions. Improving air quality in urban areas implies reducing road traffic pollutant emissions. Conventionally, public authorities and air quality experts monitor road traffic emissions using macroscopic models based on emission factors that depend solely on average speed. Unfortunately, different road sections with the same average speed can exhibit significantly variable actual pollutant emissions. To address this bias, we propose a mesoscopic model based on deep learning to compute more precise emissions from infrastructure characteristics. This model utilizes neural networks that rely on raw scalar features from geographic information systems. The first neural network outputs a driving behavior distribution, while the second provides the mean and standard deviation values of five pollutant emissions per road link and vehicle. Our global method demonstrates real-time emissions estimation with an inference computation time of 2 minutes per one-hour step for a city the size of Lyon, France (40000 road links) and 30 representative vehicles. Our methods demonstrate the ability to accurately learn driving behavior, achieving an 81% accuracy on the test set. Moreover, by accounting for driving behavior, pollutant emissions are accurately estimated, resulting in a 35% reduction in the mean absolute error for the CO2estimate compared to a macroscopic model.

Nowadays, health related issues of local pollutant emissions due to transport is becoming a major concern in our modern society. Exhaust emissions level for various gases are strongly related to the driving behavior of the user. It is of public knowledge that vehicle approval regulation techniques do not reflect actual emissions on everyday trips due, for instance, to the very smooth velocity profile considered. Available means of measuring the actual environmental footprint of a vehicle exist and are called Portable Emissions Measurement Systems (PEMS). However these sensors are usually bulky and too expensive for large scale campaigns. We propose a novel solution to monitor users environmental footprint using only a smartphone device with no additional sensors. The proposed solution is able to provide individualized feedback depending on vehicle characteristics, driving style and trips topology. The comprehensive pollutant gas emission estimation algorithm is presented including vehicle, engine, aftertreatment models and environmental footprint calculation.

Increasing urban air pollution level is one of the major concerns for citizens due to its impact on public health. In cities, vehicle emissions are one of the main contributors for nitrogen oxides (NOx) and particulate matter (PM) emissions. These emissions are not only related to the vehicle technology but also to the driving behavior of the user. For most people air pollution is barely noticeable and the driver's impact is still unknown. The contribution of this paper consists in using new information and communication technology to create an in vehicle-IoT device giving the driver a real-time feedback on his emissions and on his exposure during his trips. This is achieved by coupling complex simulation models deployed on distant servers and the existing smartphone GNSS sensor in order to get a low-cost solution, compliant with large-scale deployment. Air quality exposure is estimated using an atmospheric urban dispersion model based on the driver real-time location. Vehicle pollutant emissions are computed for each second of the trip from the measured GNSS speed, position and altitude using models simulating the physical phenomena involved in pollutant formation. Vehicle technical features are taken into account by the way of a data bank of sub-system models automatically selected and tuned based on technical parameters retrieved from the license plate number.

The observer presented in this paper, which was based on the use of three minimalistic bio-inspired Visual Motion Sensors (VMS) detecting Optic Flow (OF) cues, states was intended as a backup solution in the case of Inertial Measurement Unit (IMU) failure. Contrary to most previous Guidance Navigation and Control (GNC) solutions for planetary landing, which have involved a sensor suite including an IMU, an innovative strategy is presented here for estimating states without any need for inertial measurements, based solely on information about the relative velocity of the images of the surrounding environment. A Linear Parameter Varying (LPV) observer designed on a LPV system linearized around a reference trajectory, estimates: the ventral OF, the expansion OF and the local pitch angle. A previously developed observer was applied here to a larger class of nonlinear systems by making an ingenious change of variable. Simulations performed on a lunar landing scenario yielded satisfactory performance and showed the robustness of the OF based observer to initial uncertainties and measurement noise.

The 6-pixel low-speed Visual Motion Sensor (VMS) inspired by insects' visual systems presented here performs local 1-D angular speed measurements ranging from 1.5°/s to 25°/s and weighs only 2.8 g. The entire optic flow processing system, including the spatial and temporal filtering stages, has been updated with respect to the original design. This new lightweight sensor was tested under free-flying outdoor conditions over various fields onboard a 80 kg unmanned helicopter called ReSSAC. The visual disturbances encountered included helicopter vibrations, uncontrolled illuminance, trees, roads, and houses. The optic flow measurements obtained were finely analyzed online and also offline, using the sensors of various kinds mounted onboard ReSSAC. The results show that the optic flow measured despite the complex disturbances encountered closely matched the approximate ground-truth optic flow.