End-to-End deep neural network architectures for speed and steering wheel angle prediction in autonomous driving

The complex decision-making systems used for autonomous vehicles or advanced driverassistance systems (ADAS) are being replaced by end-to-end (e2e) architectures based on deep-neuralnetworks (DNN). DNNs can learn complex driving actions from datasets containing thousands of images and data obtained from the vehicle perception system. This work presents the classification, design and implementation of six e2e architectures capable of generating the driving actions of speed and steering wheel angle directly on the vehicle control elements. The work details the design stages and optimization process of the convolutional networks to develop six e2e architectures. In the metric analysis the architectures have been tested with different data sources from the vehicle, such as images, XYZ accelerations and XYZ angular speeds. The best results were obtained with a mixed data e2e architecture that used front images from the vehicle and angular speeds to predict the speed and steering wheel angle with a mean error of 1.06%. An exhaustive optimization process of the convolutional blocks has demonstrated that it is possible to design lightweight e2e architectures with high performance more suitable for the final implementation in autonomous driving.This work was partially supported by DGT (ref.SPIP2017-02286) and GenoVision (ref.BFU2017-88300-C2-2-R) Spanish Government projects, and the “Research Programe for Groups of Scientific Excellence in the Region of Murcia” of the Seneca Foundation (Agency for Science and Technology in the Region of Murcia—19895/GERM/15)

Electric vehicle conversion: optimisation of parameters in the design process

Jedan od pravaca ka stvaranju čistijih i ekonomičnijih vozila jeste prihvatanje koncepta električnog vozila. Rad predstavlja suvremene koncepte konstrukcije električnih vozila širom svijeta, kao i načine snabdjevanja vozila energijom. Opisan je prepravak vozila kako bi ga se moglo pogoniti elektro-motorom. Prikazani su rezultati vučno-dinamičkog proračuna i simulacije stabilnosti prije i poslije prepravka. Analizirani su dobiveni rezultati i utjecajni čimbenici radi optimiziranja u cilju utjecaja na karakteristike prepravljenog vozila. Zaključeno je da je proces optimizacije neophodno provesti prije početka prepravka kako bi se izbjegle neželjene posljedice, tj. neželjene karakteristike prepravljenog vozila i visoki troškovi prepravka.One of the directions for making cleaner and more economic vehicles is to adopt electric vehicle concept. The paper shows current design concepts for electric vehicles worldwide, as well as current sources for supplying vehicles with electric energy. It describes a conversion of one vehicle so that it can be powered by electric motor. The results of tractive and dynamic characteristics calculation and vehicle stability simulation, before and after the conversion, are shown. Obtained results and influential factors are analysed so they can be optimised in order to influence the final characteristics of the converted vehicle. The conclusion is that the complete optimisation process should be performed before the beginning of vehicle conversion in order to avoid undesirable effects, i.e. undesirable characteristics of converted vehicle and high conversion costs

Defining procedures and simulation tools to test high levels of automation for cars in realistic traffic, driving and boundary conditions

Il crescente livello di automazione nella guida dei veicoli su gomma rende sempre più complesse e articolate le procedure di testing e validazione dei dispositivi. La tendenza alla realizzazione di sistemi che sostituiscano il guidatore in tutto o in parte, determina un cambiamento paradigmatico nell'ambito della validazione, la quale non può più occuparsi esclusivamente del test del corretto funzionamento del dispositivo da validare, ma dovrà testare le logiche di guida e le "scelte" che opera al variare dei contesti. Come ampiamente evidenziato nella letteratura scientifica di settore1 i processi di validazione rappresenteranno il più grande ostacolo alla realizzazione e messa in produzione dei sistemi di quarto e quinto livello SAE2 di automazione. Numerose ricerche hanno dimostrato3 che il testing su strada non rappresenta una soluzione che possa dare risultati attendibili in tempi sufficientemente brevi, ma a tutt'oggi non esistono software sufficientemente complessi da realizzare simulazioni che tengano conto di tutte le variabili necessarie. La ricerca intende definire le corrette procedure di testing di veicoli ad elevato grado di automazione in condizioni di traffico realistiche, avvalendosi di software di simulazione specifici di ogni settore coinvolto nel processo, realizzando uno strumento di testing integrato sufficientemente efficace

A long-term energy efficiency prediction model for the Brazilian automotive industry

According to law number 12.715/2012, Brazilian government instituted guidelines for a program named Inovar-Auto. In this context, energy efficiency is a survival requirement for Brazilian automotive industry from September 2016. As proposed by law, energy efficiency is not going to be calculated by models only. It is going to be calculated by the whole universe of new vehicles registered. In this scenario, the composition of vehicles sold in market will be a key factor on profits of each automaker. Energy efficiency and its consequences should be taken into consideration in all of its aspects. In this scenario, emerges the following question: which is the efficiency curve of one automaker for long term, allowing them to adequate to rules, keep balancing on investment in technologies, increasing energy efficiency without affecting competitiveness of product lineup? Among several variables to be considered, one can highlight the analysis of manufacturing costs, customer value perception and market share, which characterizes this problem as a multi-criteria decision-making. To tackle the energy efficiency problem required by legislation, this paper proposes a framework of multi-criteria decision-making. The proposed framework combines Delphi group and Analytic Hierarchy Process to identify suitable alternatives for automakers to incorporate in main Brazilian vehicle segments. A forecast model based on artificial neural networks was used to estimate vehicle sales demand to validate expected results. This approach is demonstrated with a real case study using public vehicles sales data of Brazilian automakers and public energy efficiency data. According to our results Inovar-Auto targets will be reached in spite of little progress over last four years

Optimal driving during electric vehicle acceleration using evolutionary algorithms

Due to the limited amount of stored battery energy it is necessary to optimally accelerate electric vehicles (EVs), especially in urban driving cycles. Moreover, a quick speed change is also important to minimize the trip time. Conversely, for comfortable driving, the jerk experienced during speed changing must be minimum. This study focuses on finding a comfortable driving strategy for EVs during speed changes by solving a multi-objective optimization problem (MOOP) with various conflicting objectives. Variants of two different competing evolutionary algorithms (EAs), NSGA-II (a non-dominated sorting multi-objective genetic algorithm) and SPEA 2 (strength Pareto evolutionary algorithm), are adopted to solve the problem. The design parameters include the acceleration value(s) with the associated duration(s) and the controller gains. The Pareto-optimal front is obtained by solving the corresponding MOOP. Suitable multi-criterion decision-making techniques are employed to select a preferred solution for practical implementation. After an extensive analysis of EA performance and keeping online implementation in mind, it was observed that NSGA-II with the crowding distance approach was the most suitable. A recently proposed innovization procedure was used to reveal salient properties associated with the obtained trade-off solutions. These solutions were analyzed to study the effectiveness of various parameters influencing comfortable driving