Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Intelligent Torque Vectoring Approach for Electric Vehicles with Per-Wheel Motors

Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.The research leading to these results has been supported by the ECSEL Joint Undertaking under Grant agreement no. 662192 (3Ccar).This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation program and the ECSEL member states

Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.This research was supported by the European Project SHOW from the Horizon 2020 program under Grant Agreement No. 875530

Cooperative controllers for highways based on human experience

The AUTOPIA program has been working on the development of intelligent autonomous vehicles for the last 10 years. Its latest advances have focused on the development of cooperative manœuvres based on communications involving several vehicles. However, so far, these manœuvres have been tested only on private tracks that emulate urban environments. The first experiments with autonomous vehicles on real highways, in the framework of the grand cooperative driving challenge (GCDC) where several vehicles had to cooperate in order to perform cooperative adaptive cruise control (CACC), are described. In this context, the main challenge was to translate, through fuzzy controllers, human driver experience to these scenarios. This communication describes the experiences deriving from this competition, specifically that concerning the controller and the system implemented in a Citröen C3

Making Transport Safer: V2V-Based Automated Emergency Braking System

An important goal in the field of intelligent transportation systems (ITS) is to provide driving aids aimed at preventing accidents and reducing the number of traffic victims. The commonest traffic accidents in urban areas are due to sudden braking that demands a very fast response on the part of drivers. Attempts to solve this problem have motivated many ITS advances including the detection of the intention of surrounding cars using lasers, radars or cameras. However, this might not be enough to increase safety when there is a danger of collision. Vehicle to vehicle communications are needed to ensure that the other intentions of cars are also available. The article describes the development of a controller to perform an emergency stop via an electro-hydraulic braking system employed on dry asphalt. An original V2V communication scheme based on WiFi cards has been used for broadcasting positioning information to other vehicles. The reliability of the scheme has been theoretically analyzed to estimate its performance when the number of vehicles involved is much higher. This controller has been incorporated into the AUTOPIA program control for automatic cars. The system has been implemented in Citroën C3 Pluriel, and various tests were performed to evaluate its operation

Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms

Advanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution.Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.Te authors would like to acknowledge the ESCEL Project ENABLE-S3 (with Grant no. 692455-2) for the support in the development of this work

Novel Fuzzy Torque Vectoring Controller for Electric Vehicles with per-wheel Motors

[Resumen] The continuous innovation on the electric vehicle area has led to the use of multi-motor powertrain topologies. These powertrains imply new degrees of freedom and better controllability, thus enabling elaborate control solutions for enhanced vehicle dynamics. This allows not only to achieve better vehicle stability, but also greater performance and responsiveness, leading to safer and more satisfying vehicle handling. This work presents a Fuzzy Torque Vectoring Controller for a rear-wheel drive car with independent motors per wheel. The developed design has been validated in a high fidelity vehicle dynamics simulation platform demonstrating its capability to enhance not only curve performance, but also stability

Arbitration for balancing control between the driver and ADAS systems in an automated vehicle: Survey and approach

International audience— 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 de-pends 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 dRiVE 1 , 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

Low-speed longitudinal controllers for mass-produced cars: A comparative study

Four longitudinal control techniques are compared: a classical Proportional-Integral (PI) control; an advanced technique-called the i-PI-that adds an intelligent component to the PI; a fuzzy controller based on human experience; and an adaptive-network-based fuzzy inference system. The controllers were designed to tackle one of the challenging topics as yet unsolved by the automotive sector: managing autonomously a gasoline-propelled vehicle at very low speeds. The dynamics involved are highly nonlinear and constitute an excellent test-bed for newly designed controllers. A Citroën C3 Pluriel car was modified to permit autonomous action on the accelerator and the brake pedals-i.e., longitudinal control. The controllers were tested in two stages. First, the vehicle was modeled to check the controllers' feasibility. Second, the controllers were then implemented in the Citroën, and their behavior under the same conditions on an identical real circuit was compared

Step and curb detection for autonomous vehicles with an algebraic derivative-based approach applied on laser rangefinder data

International audiencePersonal 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