The reduction of Greenhouse Gases is of great interest inside the industry and, especially, the road transport sector. Heavy-duty vehicles have been extensively researched due to their significant contribution. This work presents a digital twin (LONGRUN simulation platform) to analyse different heavy-duty vehicle aerodynamic designs and powertrain topologies. Based on a forward-looking formulation, this work allows the analysis of novel hybridisation and electrification control strategies impact. We validated our platform components against a commercial tool (VECTO) adopted by the European Commission for combustion powertrain analysis. In this article, we provide a modular platform for heavy-duty vehicles, in a widely used software inside the automotive industry. Furthermore, our platform offers the possibility to introduce customised control strategies for hybrid-electric vehicles. This work analyses the impact of parallel and serial hybrid powertrain topologies.

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Research on Intelligent Transportation Systems (ITS) is continuously achieving a great degree of maturity for Cooperative and Connected Automated Mobility (CCAM) technology in vehicles. It includes embedded functionalities and the interaction with the environment. Therefore, this interaction should include the traffic lights information. It is a key element for the future implementation of CCAM in urban and inter-urban scenarios. In this paper, a Model Predictive Control (MPC) approach for an optimal speed behavior of an automated urban bus is presented. The traffic light information through V2I communication is used to achieve safe and comfortable behavior in the real condition demonstrator using thresholds for driving constraints in the MPC controller. Results show a good performance of our approach, validated in the city of Malaga during one month of testing a twelve-meter bus, connected with the infrastructure, in the framework of the CCAM Projects.

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority.A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years.Yet, a global synthesis on these efforts is not available.To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology.Articles from the literature are classified in theory-and application-oriented contributions.From these, a clear distinction is found between coupled and uncoupled shared control.Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface.Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems.Results show that the inclusion of a driver model helps to reduce the conflicts at the steering.Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority.Concerning the evaluation, driverin-the-loop simulators are the most common platforms, with few works performed in real vehicles.Implementation in experimental vehicles is expected in the upcoming years. Index Terms-Arbitration, driver-

Intelligent Transportation Systems (ITS) is currently one of the most active research areas, being electric vehicles (EVs) and their vehicle dynamics enhancement key topics. For this purpose, the development of optimal Advanced Driver-Assistance Systems (ADAS) and Advanced Vehicle Dynamics Control Systems (AVDC) is required. Conventionally, these systems have been focused on increasing the stability of the vehicle in critical scenarios. However, EVs enable the possibility of including also the efficiency by making use of the regenerative braking as a control variable. In order to be able to design such sophisticated control systems, it is necessary to implement control techniques capable to manage both stability and efficiency. In this sense, intelligent control techniques have demonstrated to be one of the best alternatives. In this work a Torque Vectoring (TV) algorithm based on intelligent control techniques and with regenerative braking capabilities is presented. The presented TV approach has been implemented in a embedded platform and tested in a Hardware in the Loop (HiL) setup. Results show that the presented approach is able to not only enhance the dynamics vehicle behaviour in a challenging emergency manoeuvre, but also to increase its overall efficiency.

In this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.

External sensing for automative applications are key tools for the development of Advanced Driver Assistance Systems (ADAS), since they can sense and analyse the environment around the vehicle by providing pictures of the scene behind the vehicle. Parking assistance systems are already available in the market. However, most of these applications are based on ultrasonic sensors, wide-angle image cameras, RADAR, etc, which present some drawbacks such as dependency to light conditions or high maintenance cost, among others. This paper proposes an approach for assisting drivers to park through the processing of data derived by a 3D Time-of-Flight (ToF) camera and the reconstruction of the objects identified around the vehicle. The proposed technique is focused on fusion of two parallel processing technologies, a visual one through the intensity image and a spatial one through the point cloud. Both of them are centered on the detection of the vehicle's plate to estimate its position and determine free spots in the parking. This novel methodology improves the detection of surrounding elements, since it helps to solve two main problems with this kind of devices: 1) the degraded performance under bright ambient light problem (occurring mainly in outdoor parkings), that causes shadows and brightness in the images, hindering its process to detect objects; and 2) the limited detection of low reflection objects such as dark cars. Moreover, this fusion allows to link each pixel of the image with a 3D position, and vice versa, giving the point cloud a visual reference. The system is evaluated through a Renault Twizy platform in real conditions.

Nowadays, intelligent transportation systems (ITS) have become one of the main research areas, being electric vehicles (EVs) and automated vehicles key topics. To guarantee safety and comfort and maximize their efficiency, proper vehicle dynamics control systems such as Torque Vectoring (TV) are mandatory. This work proposes an intelligent TV approach for EVs which considers the vertical force distribution among the tractive wheels. This approach allows to maximize vehicle cornering capacity and also its efficiency. In order to demonstrate its effectiveness, its performance is compared using Dynacar High Fidelity vehicle simulator with three traditional approaches found in the literature: PID, Second Order Sliding Mode Control (SOSMC) and Fuzzy Control. Results show that all evaluated controllers improve the handling of the vehicle and the efficiency with respect to the baseline vehicle. However, the proposed intelligent TV system provides better overall results.

The following topics are dealt with: CMOS integrated circuits; low-power electronics; analogue-digital conversion; integrated circuit design; field programmable gate arrays; neural nets; learning (artificial intelligence); CMOS analogue integrated circuits; CMOS digital integrated circuits; neurophysiology.

Overtaking is one of the most difficult tasks during driving. This manoeuvre demands good skills to accomplish it correctly. In the overtaking considering multiple vehicles (more than a couple) is necessary to understand, predict and coordinate future actions of the other participants. These reasons make it a significant scenario for testing in the connected and automated driving field, with the main goal of predicting safe future states. In this sense, this work presents an overtaking method based on a linear Model Predictive Control (MPC) approach, which considers multiple participants involved in the scenario. This method adapts dynamically the trajectory for the manoeuvre in case of unexpected situations. Some of these changes consider other vehicles coming on the opposite lane or variations on participants' driving decisions. Additionally, the system considers passengers' comfort, the vehicle physical constraints and lateral actions of the vehicle decoupled of the longitudinal ones to simplify the problem.

Advanced Driver Assistance Systems (ADAS) and automated vehicle applications based on embedded sensors have become a reality today. As road vehicles increase its autonomy and the driver shares his role in the control loop, novel challenges on their dependability assessment arise. One key issue is that the notion of controllability becomes more complex when validating the robustness of the automated vehicle in the presence of faults. This paper presents a simulation-based fault injection approach aimed at finding acceptable controllability properties for the model-based design of control systems. We focus on determining the best fault models inserting exceptional conditions to accelerate the identification of specific areas for testing. In our work we performed fault injection method to find the most appropriate safety concepts, controllability properties and fault handling strategies at early design phases of lateral control functions based on the error in the Differential GPS signal.

Nowadays, the development of Intelligent Transportation System (ITS) is increasing due to its versatility, adaptability and use of clean energy. There are a number of pass and on-going projects worldwide dealing with the different challenges and approaches to solve road transport-related issues. Some of them are dealing with the Cybernetic Transportation Systems (CTS), which is an urban mobility concept based on the automation of door-to-door transport systems i.e. the Cybercars as a two-passenger CTS. This paper presents the functional architecture of the CTSs and the development of an automated global planner. Specifically, a new approach that considers the automatization of the global planner stage, which allows path calculations and modifications in real time, considering on-demand stopping points. The experimental tests show a proper behaviour in our facilities at INRIA-Rocquencourt (France).

Vehicle electrification is proliferating and evolving towards multi-motor vehicles. This implies new degrees of freedom and higher controllability. Meanwhile, embedded technology is presenting attractive new solutions. Furthermore, Model-Based Development gains relevance as the complexity of automotive applications continues to grow and regulations address functional safety. The implications of this context are described in this work and taken as motivation to implement a solution that involves the key technologies related to the discussed domains and their remarkable potential. This paper presents the implementation of a methodology and its workflow with a toolchain setup which has been used to develop and commission a System-on-Chip-based ECU for a Torque Vectoring algorithm in a Motor-in-Wheel demonstrator vehicle. The implemented solution permits to rapidly and safely move from algorithm development and laboratory simulations to real circuit testing, agilely iterating control enhancements, tests and validation, for an efficient and effective deployment. Furthermore, both the applied methodology and the selected toolchain are suited for future certification considerations.

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

Power consumption and battery life are two of the key aspect when it comes to improve electric transportation systems autonomy. This paper describes the design, development and implementation of a speed profile generation based on the calculation of the optimal energy consumption for electric Cybercar vehicles for each of the stretches that are covering. The proposed system considers a commuter daily route that is already known. It divides the pre-defined route into segments according to the road slope and stretch length, generating the proper speed reference. The developed system was tested on an experimental electric platform at Inria's facilities, showing a significant improvement in terms of energy consumption for a pre-defined route.

Automated functions for real scenarios have been increasing in last years in the automotive industry. Many research contributions have been done in this field. However, other problems have come to the drivers: When should they (the drivers or the new automated systems) be able to take control of the vehicle? This question has not a simple answer; it depends on different conditions, such as: the environment, driver condition, vehicle capabilities, fault tolerance, among others. For this reason, in this work we will analyze the acceptability to the ADAS functions available in the market, and its relation with the different control actions. In this paper a survey on arbitration and control solutions in ADAS is presented. It will allow to create the basis for future development of a generic ADAS control (the lateral and longitudinal behavior), based on the integration of the application request, the driver behavior and driving conditions in the framework of the DESERVE project (DEvelopment platform for Safe and Efficient dRiVE1, a ARTEMIS project 2012–2105). The main aim of this work is to allow the development of a new generation of ADAS solutions where the control could be effectively shared between the vehicle and the driver. Different solutions of shared control have been analyzed. A first approach is proposed, based on the presented solutions.

In this paper, an algorithm for dynamic path generation in urban environments is presented, taking into account structural and sudden changes in straight and bend segments (e.g. roundabouts and intersections). The results present some improvements in path generation (previously hand plotted) considering parametric equations and continuous-curvature algorithms, which guarantees a comfortable lateral acceleration. This work is focused on smooth and safe path generation using road and obstacle detection information. Finally, some simulation results show a good performance of the algorithm using different ranges of urban curves. The main contribution is an Intelligent Trajectory Generator, which considers infrastructure and vehicle information. This method is recently used in the framework of the project CityMobil21, for urban autonomous guidance of Cybercars.

Nowadays, some developments in the vehicle industry permit a safe and comfortable driving. However, several manufactures and research groups are still working in the improvement of the control strategies and path smoothing algorithms. In this paper, a new trajectory generation approach for autonomous vehicles in urban scenarios, considering parametric equations, is proposed. An algorithm that considers Bezier curves and circumference parametric equations for a real vehicle, specifically in roundabout and urban intersections is presented. This approach is generated in real time and can be adapted to dynamic changes in the route. A smooth trajectory generator computationally efficient and easily implementable is proposed. Moreover, this new trajectory generator reduces the control actions, generated with to a fuzzy controller. Some trials have been performed in an urban circuit with promising performance.

Nowadays, the insertion of Intelligent Transportation System (ITS) in the society is a fact, due to a big number of projects, demonstrations and researches that have been carried out around the world. The Cybernetic Transportation Systems (CTS) are part of the ITS, and these have especial attention thanks to its versatility, adaptability and clean energy characteristics. This paper aims to describe the evolution of the on-demand door-to-door transport systems, with Cybercars, in the framework of the IMARA team. A review of different approaches and the description of the last projects and scenarios with CTSs are considered. This work focuses in a new approach that contemplates a modular architecture, which allows parametric curve generation in real time, considering the different environment conditions-obstacle detections, shape of the road, among others. This architecture has been tested in simulations. These results promise good behavior for its future implementation in real urban scenarios, in the framework of the CityMobil2 project1.

Personal Mobility Vehicles (PMV) is is an important part of the Intelligent Transportation System (ITS) domain. These new transport systems have been designed for urban traffic areas, pedestrian streets, green zones and private parks. In these areas, steps and curbs make the movement of disable or mobility reduced people with PMV, and with standard chair wheels difficult. In this paper, we present a step and curb detection system based on laser sensors. This system is dedicated to vehicles able to cross over steps, for transportation systems, as well as for mobile robots. The system is based on the study of the first derivative of the altitude and highlights the use of a new algebraic derivative method adapted to laser sensor data. The system has been tested on several real scenarios. It provides the distance, altitude and orientation of the steps in front of the vehicle and offers a high level of precision, even with small steps and challenging scenarios such as stairs.

Autonomous navigation is one of the most important challenges in the outdoor mobile robot field. For an automatic vehicle (which can be considered a type of outdoor mobile robot), path following can be implemented using global positioning systems (GPS) to allow the configuration of different navigation styles such as the shortest or fastest route, toll avoidance, etc., and even the definition of new routes. The main problem is when an unexpected circumstance occurs - traffic accident, road closure, etc. This paper presents an autonomous vehicle guidance system based on fuzzy logic systems to resolve unexpected road situations. A fuzzy steering controller performs the autonomous navigation, allowing reverse as well as forward driving in urban environments. Good performance was obtained in trials performed with a commercial electric Citroën Berlingo van on a private driving circuit.

This work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic aspect of car driving. Moreover, this modularity of the architecture will allow us to replace or add modules in the future as a way to enhance particular features of particular situations. In the present work, some of the modules are implemented by means of hand-designed driving heuristics, whereas modules responsible for adapting the speed and direction of the vehicle to the track's shape, both critical aspects of driving a vehicle, are optimized by means of a genetic algorithm that evaluates the performance of the controller in four different tracks to obtain the best controller in a large number of situations; the algorithm also penalizes controllers that go out of the track, lose control, or get damaged. The evaluation of the performance is done in two ways. First, in runs with and without adversaries over several tracks. And second, the architecture was submitted as a participant to the 2010 Simulated Car Racing Competition, which in end won laurels. © 2012 Wiley Periodicals, Inc.

Vehicles equipped with intelligent systems designed to prevent accidents, such as collision warning systems (CWSs) or lane-keeping assistance (LKA), are now on the market. The next step in reducing road accidents is to coordinate such vehicles in advance not only to avoid collisions but to improve traffic flow as well. To this end, vehicle-to-infrastructure (V2I) communications are essential to properly manage traffic situations. This paper describes the AUTOPIA approach toward an intelligent traffic management system based on V2I communications. A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed. The proposed solution was validated by an IEEE-802.11p-based communications study. The entire system showed good performance in testing in real-world scenarios, first by computer simulation and then with real vehicles.

Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.

Traffic merging in urban environments is one of the main causes of traffic congestion. From the driver's point of view, the difficulty arises along the on-ramp where the merging vehicle's driver has to discern whether he should accelerate or decelerate to enter the main road. In parallel, the drivers of the vehicles already on the major road may have to modify their speeds to permit the entrance of the merging vehicle, thus affecting the traffic flow. This paper presents an approach to merging from a minor to a major road in congested traffic situations. An automated merging system that was developed with two principal goals, i.e., to permit the merging vehicle to sufficiently fluidly enter the major road to avoid congestion on the minor road and to modify the speed of the vehicles already on the main road to minimize the effect on that already congested main road, is described. A fuzzy controller is developed to act on the vehicles' longitudinal control - throttle and brake pedals - following the references set by a decision algorithm. Data from other vehicles are acquired using wireless vehicle-to-infrastructure (V2I) communication. A system installed in the infrastructure that is capable of assessing road traffic conditions in real time is responsible for transmitting the data of the vehicles in the surrounding area. Three production vehicles were used in the experimental phase to validate the proposed system at the facilities of the Centro de Automática y Robótica with encouraging results.

Research on intelligent transport systems (ITSs) is steadily leading to safer and more comfortable control for vehicles. Systems that permit longitudinal control have already been implemented in commercial vehicles, acting on throttle and brake. Nevertheless, lateral control applications are less common in the market. Since a too-sudden turn of the steering wheel can cause an accident in a few seconds, good speed and position control of the steering wheel is essential. We present here a new cascade control architecture based on fuzzy logic controllers that emulate a human driver's behavior. The control architecture was tested on a real vehicle at different vehicle speeds. The results showed the use of a straightforward and intuitive fuzzy controller to give good performance.

The control of nonlinear systems has been putting especial attention in the use of Artificial Intelligent techniques, where fuzzy logic presents one of the best alternatives due to the exploit of human knowledge. However, several fuzzy logic real-world applications use manual tuning (human expertise) to adjust control systems. On the other hand, in the Intelligent Transport Systems (ITS) field, the longitudinal control (throttle and brake management) is an important topic because external perturbations can generate uncomfortable accelerations as well as unnecessary fuel consumption. In this work, we utilize a neuro-fuzzy system to use human driving knowledge to tune and adjust the input-output parameters of a fuzzy if-then system. The neuro-fuzzy system considered in this work is ANFIS (Adaptive-Network-based Fuzzy Inference System). Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous manual tuned controller, mainly in comfort and efficient use of actuators.

A major research topic in intelligent transportation systems (ITSs) is the development of systems that will be capable of controlling the flow of vehicular traffic through crossroads, particularly in urban environments. This could significantly reduce traffic jams, since autonomous vehicles would be capable of calculating the optimal speed to maximize the number of cars driving through the intersection. We describe the use of vehicle-to-vehicle (V2V) communications to determine the position and speed of the vehicles in an environment around a crossroad. These data are used to estimate the intersection point, and a fuzzy controller then modifies the speed of the cars without right of way according to the speed of the car with right of way. Experimental tests conducted with two mass-produced cars on a real circuit at the facilities of the Instituto de Automa¿tica Industrial, Consejo Superior de Investigaciones Cienti¿ficas, Madrid, Spain, gave excellent results.

The techniques of soft computing are recognized as having a strong learning and cognition capability as well as good tolerance to uncertainty and imprecision. These properties allow them to be applied successfully to intelligent transportation systems (ITS), a broad range of diverse technologies that designed to answer many transportation problems. The unmanned control of the steering wheel is one of the most important challenges faced by researchers in this area. This paper presents a method of automatically adjusting a fuzzy controller to manage the steering wheel of a mass-produced vehicle. Information about the state of the car while a human driver is handling it is captured and used to search, via genetic algorithms, for the best fit of an appropriate fuzzy controller. Evaluation of the fuzzy controller will take into account its adjustment to the human driver's actions and the absence of abrupt changes in its control surface, so that not only is the route tracking good, but the drive is smooth and comfortable for the vehicle's occupants.