Olivier Orfila

A new eco-driving assistance system for a light vehicle: Energy management and speed optimization

This paper presents a new method to model and optimize the vehicle fuel consumption and its speed in the design of an eco-driving assistance system (EDAS) developed within the EU ecoDriver project. The main objective of this EDAS is to combine a precise fuel consumption model with a robust optimization module. An optimal speed profile is obtained to reduce the energy consumption. The gear management is also included in this procedure. The instantaneous fuel consumption rate is expressed as a piecewise polynomial of the instantaneous engine speed and engine torque. A dynamic programming technique is used to optimize the vehicle fuel consumption considering the safety requirements. The real vehicle experiments show the good performance of the piecewise model. The algorithm is implementable in a light vehicle Renault Clio 3.

Smart and Green ACC, Safety and Efficiency for a Longitudinal Driving Assistance

Driving Assistances aim at enhancing the driver safety and the comfort. Nowadays, the consumption is also a major criterion which must be integrated in the driving assistances. Then, we propose to redefine the behavior of an ACC with energy efficiency consideration to perform a Smart and Green ACC. We apply our development to the specific use case of the electric vehicle that allows regenerative braking. The ACC, once activated, operates under two possible modes (speed control and headway spacing control). We define the behavior of the driving assistance under these both possible modes, focusing on the distance control. We present the efficiency of various strategies without trading off safety. We conclude on the efficiency by presenting several use cases that show the SAGA behavior.

Smart and Green ACC, adaptation of the ACC strategy for electric vehicle with regenerative capacity

This paper presents an optimization of a conventional Adaptive Cruise Control system (ACC) for the specific use of electric vehicles with regenerative capacity, namely the Smart and Green ACC (SAGA). Longitudinal control strategies, that are developed for the driving assistances, mainly aim at optimizing the safety and the comfort of the vehicle occupants. Electric vehicles have the possibility, depending on the architecture, the speed and the braking demand, to regenerate a part of the electric energy during the braking. Moreover, the electric vehicle range is currently limited. The opportunity to adapt the braking of an ACC system to extend slightly the range must not be avoided. When the ACC is active, the vehicle speed is controlled automatically either to maintain a given clearance to a forward vehicle, or to maintain the driver desired speed, whichever is lower. We define how we can optimize both mode and what is the impact, in term of safety and strategy, including the knowledge of the future of the road, integrating a navigation system.

Ecodriving performances of human drivers in a virtual and realistic world

In this study, results of an ecodriving challenge that took place during the Paris Motor Show in 2014 are presented. The principle of this challenge was to drive a simulated passenger car as far as possible with a limited quantity of energy (15 cL). The experimental setup, constituted of the SiVIC software, an Oculus Rift Helmet and a fuel consumption model, is also detailed. 1211 trips of visitors were validated during the 17 days of the event. Results showed that high acceleration without kickdown is desirable and that constant speed can lead to significant reduction in energy consumption. Next work will concentrate on improving the simulation and the scenario to increase the immersion realism and the ecodriving behavior sensitivity.

Smart and Green Adaptive Cruise Control for an Electric Vehicle: first results

This paper concerns the development of the Smart and Green Adaptive Cruise Control aiming at controlling the ego vehicle speed taking into account the road data in order to optimize the energy consumption while improving safety (no collision, under the legal speed, up to the safe headway). A graph search algorithm of Dijkstra is used to build up a velocity pattern offering the best compromise between energy consumed and covered time.

Perception, information processing and modeling: Critical stages for autonomous driving applications

Over the last decades, the development of Advanced Driver Assistance Systems (ADAS) has become a critical endeavor to attain different objectives: safety enhancement, mobility improvement, energy optimization and comfort. In order to tackle the first three objectives, a considerable amount of research focusing on autonomous driving have been carried out. Most of these works have been conducted within collaborative research programs involving car manufacturers, OEM and research laboratories around the world. Recent research and development on highly autonomous driving aim to ultimately replace the drivers actions with robotic functions. The first successful steps were dedicated to embedded assistance systems such as speed regulation (ACC), obstacle collision avoidance or mitigation (Automatic Emergency Braking), vehicle stability control (ESC), lane keeping or lane departure avoidance. Partially automated driving will require co-pilot applications (which replace the driver on his all driving tasks) involving a combination of the above methods, algorithms and architectures. Such a system is built with complex, distributed and cooperative architectures requiring strong properties such as reliability and robustness. Such properties must be maintained despite complex and degraded working conditions including adverse weather conditions, fog or dust as perceived by sensors. This paper is an overview on reliability and robustness issues related to sensors processing and perception. Indeed, prior to ensuring a high level of safety in the deployment of autonomous driving applications, it is necessary to guarantee a very high level of quality for the perception mechanisms. Therefore, we will detail these critical perception stages and provide a presentation of usable embedded sensors. Furthermore, in this study of state of the art of recent highly automated systems, some remarks and comments about limits of these systems and potential future research ways will be provided. Moreover, we will also give some advice on how to design a co-pilot application with driver modeling. Finally, we discuss a global architecture for the next generation of co-pilot applications. This architecture is based on the use of recent methods and technologies (AI, Quantify self, IoT …) and takes into account the human factors and driver modeling.

Proposal of a virtual and immersive 3D architecture dedicated for prototyping, test and evaluation of eco-driving applications

Simulation has been widely used to estimate the benefits of ADAS with embedded sensors or more recently Cooperative Systems based on Inter-Vehicular Communications. This paper presents the proposal of a new architecture built with the both SiVIC and RTMaps platforms in order to prototype, to test and to validate eco-driving applications. In this architecture, the innovation is mainly due to the real-time immersion of a real driver in a 3D virtual environment. In this “Hardware In the Loop” platform, major contributions have been made about vehicle modeling updating, interconnection between SiVIC and an HMD. Moreover, a first modeling of a consumption sensor is proposed and used in order to achieve some tests of fuel consumption on the virtual Satorys track. All these contributions have been tuned with on-road measurements to improve reality of the scenarios. We discuss the results of a simple eco-driving scenario implemented to validate our architectures capabilities.

Eco-Driving Assistance System: A New Way of How to Save Energy

The work developed in this paper is realized within the ecoDriver EU FP7 project that is shortly presented in the first part of the paper. Among the several main objectives underlined by this project, one consists in developing a vehicle and energy consumption model to be validated and then used to help the driver to better drive in terms of consumption and safety, advised by a HMI (Human-Machine Interface) module. Several experimental results are shown to illustrate the obtained energy saving with such an EDAS and the legal speed respect.Copyright