Articles

Abstract

For a decade, researchers have focused on the development and deployment of road automated mobility. In the development of autonomous driving embedded systems, several stages are required. The first one deals with the perception layers. The second one is dedicated to the risk assessment, the decision and strategy layers and the optimal trajectory planning. The last stage addresses the vehicle control/command. This paper proposes an efficient solution to the second stage and improves a virtual Cooperative Pilot (Co-Pilot) already proposed in 2012. This paper thus introduces a trajectory planning algorithm for automated vehicles (AV), specifically designed for emergency situations and based on the Autonomous Decision-Support Framework (ADSF) of the EU project Trustonomy. This algorithm is an extended version of Elastic Band (EB) with no fixed final position. A set of trajectory nodes is iteratively deduced from obstacles and constraints, thus providing flexibility, fast computation, and physical realism. After introducing the project framework for risk management and the general concept of ADSF, the emergency algorithm is presented and tested under Matlab software. Finally, the Decision-Support framework is implemented under RTMaps software and demonstrated within Pro-SiVIC, a realistic 3D simulation environment. Both the previous virtual Co-Pilot and the new emergency algorithm are combined and used in a near-accident situation and shown in different risky scenarios.

Links

Links

Abstract

Ridesplitting, a form of ridesourcing services that matches riders with similar routes to the same driver, is a high occupancy travel mode that can bring considerable benefits. However, the current ratio of ridesplitting in the ridesourcing services is relatively low and its influencing factors remain unrevealed. Therefore, this paper uses a machine learning method, gradient boosting decision tree (GBDT) model, to explore the nonlinear effects of built environment on the ridesplitting ratio of origin–destination pairs (census tract to census tract). The GBDT model also provides the relative importance ranking of all the built environment factors. The results indicate that distance to city center, land use diversity and road density are the key influencing factors of ridesplitting ratio. In addition, the non-linear thresholds of built environment factors are identified based on partial dependence plots, which could provide policy implications for the government and transportation network companies to promote ridesplitting.

Links

Links

Résumé

With the growing development of Cooperative, Connected, and Automated Mobility (CCAM), questions arise about the real impact of this innovative mobility on our daily life. CCAM originally promised to improve road safety. It is now a holistic solution for future mobility: the CCAM is there to optimize traffic, which can translate into strategies for reducing energy consumption or polluting emissions, without compromising road safety. The capability of CCAM is dependent on the reliability and robustness of its components, as it will be making life-impacting decisions. It is therefore necessary to be able to guarantee a high-level quality of sensors, communication, software, and hardware architecture. In this mobility ecosystem, the infrastructure and data that it will be able to produce is at the heart of current research issues. This paper addresses the following question: Are the Connected and automated Vehicles (CAVs) the silver bullet solution with which to answer the issues of the current mobility systems? This question is discussed by investigating the technologies used, the digital infrastructures, its robustness to cyber-attack, and their relationship with the claimed benefits on safety, energy and pollution management, traffic optimization, deployment strategy, and a link with the new generation of road infrastructures.

Links

Links

Links

Résumé

The urban transport sector has become one of the major contributors to global CO2 emissions. This paper investigates the driving forces of changes in CO2 emissions from the passenger transport sectors in di?erent cities, which is helpful for formulating effective carbon-reduction policies and strategies. The logarithmic mean Divisia index (LMDI) method is used to decompose the CO2 emissions changes into five driving determinants: Urbanization level, motorization level, mode structure, energy intensity, and energy mix. First, the urban transport CO2 emissions between 1960 and 2001 from 46 global cities are calculated. Then, the multiplicative decomposition results for megacities (London, New York, Paris, and Tokyo) are compared with those of other cities. Moreover, additive decomposition analyses of the 4 megacities are conducted to explore the driving forces of changes in CO2 emissions from the passenger transport sectors in these megacities between 1960 and 2001. Based on the decomposition results, some e?ective carbon-reduction strategies can be formulated for developing cities experiencing rapid urbanization and motorization. The main suggestions are as follows: (i) Rational land use, such as transit-oriented development, is a feasible way to control the trip distance per capita; (ii) fuel economy policies and standards formulated when there are oil crisis are e?ective ways to suppress the increase of CO2 emissions, and these changes should not be abandoned when oil prices fall; and (iii) cities with high population densities should focus on the development of public and non-motorized transport.

Links

Résumé

This paper proposes and evaluates an algorithm called Multi-Objective planning based on Simulated Annealing (MOSA) that plans a trajectory (speed profile) for a passenger car on a free, single lane road. This algorithm is relying on a decomposition of the decision space into “chunks” that are optimized separately. Two objectives have been taken into account: travel time and fuel consumption. Optimization constraints are built from safety modelings combining legal speed, curves speed limits and junctions limits. The multi-objective optimization is performed through a linear scalairisation method and the optimization is a parametric optimization based on simulated annealing. The algorithm has been tested on simulated annealing convergence and results show a good convergence under 500 iterations and a small sensitivity to variables initialization. However, sensitivity to core parameters of the simulated annealing (initial temperature and temperature decreasing rate) is very high and some guidelines for the calibration of these parameters are given in this paper. Then, the algorithm has been tested and compared to experimental results and it shows that, even if some drivers can drive the road quicker than the algorithm, they cannot drive with a lower fuel consumption. Furthermore, the algorithm results are better than the most of the experimental results according to the Pareto definition of dominance and global results outperform results from another planning algorithm based on Dijkstra’s algorithm. Future works will concentrate on improving the algorithm to be more reactive to unexpected obstacles and more consistant in the “chunks” transitions.

Links

Résumé

Ridesourcing services play a crucial role in metropolitan transportation systems and aggravate urban traffic congestion and air pollution. Ridesplitting is one possible way to reduce these adverse effects and improve the transport efficiency, especially during rush hours. This paper aims to explore the potential of ridesplitting during peak hours using empirical ridesourcing data provided by DiDi Chuxing, which contains complete datasets of ridesourcing orders in the city of Chengdu, China. A ridesplitting trip identification algorithm based on a shareability network is developed to quantify the potential of ridesplitting. Then, we evaluate the gap between the potential and actual scales of ridesplitting. The results show that the percentage of potential cost savings can reach 18.47% with an average delay of 4.76 min, whereas the actual percentage is 1.22% with an average delay of 9.86 min. The percentage of shared trips can be increased from 7.85% to 90.69%, and the percentage of time savings can reach 25.75% from 2.38%. This is the first investigation of the gap between the actual scale and the potential of ridesplitting on a city scale. The proposed ridesplitting algorithm can not only bring benefits on a city level but also take passenger delays into consideration. The quantitative benefits could encourage transportation management agencies and transportation network companies to develop sensible policies to improve the existing ridesplitting services.

Links

Links

Abstract

Speed variations are considered as an alternative for reducing fuel consumption during the use phase of passenger cars. It explores vehicle engine operating zones with lower fuel consumption, thus making possible a reduction in fuel consumption when compared to constant speed operation. In this paper, we present an evaluation of two conditions of speed variations: 50–70?km/h and 90–110?km/h using numerical simulations and controlled tests. The controlled tests performed on a test track by a professional pilot show that a reduction in fuel consumption is achievable with a conventional gasoline passenger car, with no adaptations for realizing speed variations. Numerical simulations based on a backward quasi-static powertrain model are used to evaluate the potential of speed variations for reducing fuel consumption in other speed variation conditions. When deceleration is performed with gear in neutral position, simulations show that speed variations are always correlated to a lower fuel consumption. This was suspected through previous numerical tests or evaluation on test bench but not in controlled tests conditions.

Links

Résumé

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 driver's 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.

Links

Links

Abstract

This article presents a fuel consumption model, SEFUM (Semi Empirical Fuel Use Modeling), and its comparison with three models from the literature on a 600 km experimental database. This model is easy to calibrate with only a few required parameters that are provided by car manufacturers. The test database has been built from 21 drivers who drove in two conditions (normal and ecodriving) on a 15 km trip. For the model evaluation, three indicators have been selected: instantaneous fuel use root mean square error, cumulated error and computation time in order to evaluate the accuracy both in cumulated and instantaneous fuel use and to estimate computation time of each model. Results tend to prove that the model is able to compute rapidly (maximum of 1500 simulated kilometers under Matlab) in comparison to all other models while ensuring a high accuracy and precision for cumulated and instantaneous fuel use.

Links

Résumé

L’automatisation de la conduite, jadis synonyme de science-fiction, est maintenant attendue par le grand public comme une solution miracle aux problèmes de sécurité routière, d’impact du transport sur l’environnement et de congestion des infrastructures routières. Cependant, des problèmes majeurs, liés à la technologie, la législation, l’éthique et son coût s’opposent encore à sa commercialisation de masse.

Abstract

Recent improvements of communication technologies leads to several innovations in road vehicles energy consumption. As an example, several ecodriving applications already appeared on all smartphone application markets. Using embedded smartphone signals, such applications provide real time feedback to drivers according to their performances. However most of these applications does not take into account upcoming events such as curves, slopes or crossings to advise the driver on the best actions to undertake to lower energy consumption. Furthermore, they do not analyze data coming from vehicle sensors. In this paper, we present an android application, developed within the FP7 European project ecoDriver, which provides several innovative properties: advice according to upcoming events, a real time evaluation of the driving behavior, the analysis of past actions, an interface with OBD2 connector, and some more. This paper further develops the complete architecture and links between each innovative function. Future works will concentrate on integrating image processing in this application in order to detect the possible presence of a front vehicle.

Abstract

A method of determining information relating to a path of a road vehicle, the method comprising a step a): determining at least two possible paths for the vehicle, referred to as “reference” paths; wherein the method further comprises a step e): determining information relating to an intermediate path lying between the reference paths and as a function of the reference paths.

Links

Abstract

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.

Links

Abstract

Ecodriving is known as a way to quickly and efficiently reduce fuel consumption for the concerned ecodriven vehicle. However, the impact of ecodriving on a whole road network at a large scale is unknown. In order to perform studies in a micro traffic simulation software, a fuel consumption model coupled to a gear behavior model are required. This study presents a gear shifting behavior model able to represent as well the variability of drivers as the difference between ecodriving and normal driving. This work, based on the evaluation of the real driver behaviors during 42 trips, has been partially validated with a result of 60% of time spend in the correct gear. Future works will be concentrated on a detailed validation of this model and on its implementation with a fuel consumption model.

Abstract

Usually, road safety is assessed by following adequate highway geometric design standards and can be controlled later by measurement and expertise. Nevertheless, interactions between vehicle dynamics and road characteristics cannot be simultaneously analyzed for these two means of safety evaluation. In this study, an analytical method based on road/vehicle physical interactions applied to road diagnosis is proposed. Vehicle “point” and “bicycle” models are used in this first approach. French highway geometric design standards and a statistical method are presented and evaluated on a real curve case. The proposed numerical criterion, for the “bicycle” model, is then compared to these two classical methods for the considered road section. Its advantages are that it takes into account several combined parameters, that road defects are precisely localized and that it provides hierarchically classified solutions to the road managers. After this comparison step, further improvements should be focused on the modeling of successive curves and on the improvement of the informations given to the road manager.

Links

Résumé

La connaissance des efforts exercés par les roues d'un véhicule sur une chaussée facilite l'évaluation des risques de perte de contrôle. En particulier, pour des études expérimentales, de tels efforts mesurés peuvent être rapportés aux dérives des pneumatiques pour consolider l'analyse du comportement dynamique d'un véhicule. Face aux coûteuses roues dynamométriques traditionnellement employées pour cela, un système alternatif a été développé au LCPC. Constitué d'un élément de chaussée instrumenté, ce système permet la mesure des composantes du torseur de contact entre un pneumatique et une chaussée. Sa validation est ici établie en le confrontant à un véhicule muni d'une roue dynamométrique. Des résultats sont présentés pour des situations de braquage pur d'une roue, de roulement libre et de freinage d'urgence, en contact sec ou lubrifié. Bien qu'il ne donne pas accès à la connaissance des efforts tout au long d'une trajectoire, un tel système intégré à la chaussée permet d'acquérir localement le torseur de contact avec un ordre de précision semblable à celui d'une roue dynamométrique, et ce pour tout véhicule, même dénué d'instrumentation. L'emploi de ce système sur une section routière pourrait autoriser l'évaluation de celle-ci vis-à-vis des sollicitations mobilisées par un ensemble varié de véhicules. En perspectives d'application, il serait possible de compléter les observatoires de vitesse existants sur le réseau routier par des mesures de l'adhérence réellement mobilisée par les divers usagers, afin de mieux appréhender leur prise de risque.