Towards a Decision-Making Algorithm for Automatic Lane Change Manoeuvre Considering Traffic Dynamics

This paper proposes a novel algorithm for decision-making on autonomous lane change manoeuvre in vehicles. The proposed approach defines a number of constraints, based on the vehicle’s dynamics and environmental conditions, which must be satisfied for a safe and comfortable lane change manoeuvre. Inclusion of the lateral position of other vehicles on the road and the tyre-road friction are the main advantages of the proposed algorithm. To develop the lane change manoeuvre decision-making algorithm, first, the equations for the lateral movement of the vehicle in terms of manoeuvre time are produced. Then, the critical manoeuvring time is calculated on the basis of the constraints. Finally, the decision is made on the feasibility of carrying out the manoeuvre by comparing the critical times. Numerous simulations, taking into account the tyre-road friction and vehicles’ inertia and velocity, are conducted to compute thecritical times and a model named TUG-LCA is presented based on the corresponding results

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega trends in automotive engineering and they are strongly connected to each other [...

Desarrollo de un modelo ACC para un software de simulación de dinámica de vehículos

El principal objetivo de este proyecto es el desarrollo de un modelo ACC (Automated Cruise Control) dentro de un programa de simulacion de la conduccion de un productor comercial de software. Este software sera utilizado en un simulador de conduccion cuyo objetivo es investigar la interaccion del conductor con el sistema ACC para diferentes situaciones de conduccion. El objetivo de este PFC es implementar el modelo ACC, que consiste en un sensor radar, un control de velocidad y un control de distancia. Ademas un modelo de sistema de frenada de emergencia ha sido implementado para actuar en situaciones crticas, es el denominado AEB (Autonomous Emergency Braking). Cuando no hay ningun objeto delante, el ACC mantiene una velocidad constante que es establecida por el conductor (del mismo modo que lo hace un cruise control estandar). Sin embargo, cuando se detecta un vehculo mas lento circulando en el mismo carril, el sistema frena automaticamente y mantiene una cierta distancia. Si este vehculo deja de estar presente, por ejemplo porque cambia de carril, el sistema vuelve a acelerar otra vez hasta que alcanza la velocidad de crucero establecida. El objetivo del sensor radar es detectar vehculos y su velocidad relativa basandose en el rango geometrico del radar, as como los objetos relevantes, por ejemplo el vehculo mas cercano de aquellos que circulan en el mismo carril. Por otro lado el AEB es un sistema de ayuda al conductor en situaciones de emergencia. Dicho sistema tambien hace uso del sensor radar para detectar e identicar situaciones de peligro, y en caso necesario aplicar los frenos para evitar una colision

Impact of Road Marking Retroreflectivity on Machine Vision in Dry Conditions: On-Road Test

(1) Background: Due to its high safety potential, one of the most common ADAS technologies is the lane support system (LSS). The main purpose of LSS is to prevent road accidents caused by road departure or entrance in the lane of other vehicles. Such accidents are especially common on rural roads during nighttime. In order for LSS to function properly, road markings should be properly maintained and have an adequate level of visibility. During nighttime, the visibility of road markings is determined by their retroreflectivity. The aim of this study is to investigate how road markings’ retroreflectivity influences the detection quality and the view range of LSS. (2) Methods: An on-road investigation comprising measurements using Mobileye and a dynamic retroreflectometer was conducted on four rural roads in Croatia. (3) Results: The results show that, with the increase of markings’ retroreflection, the detection quality and the range of view of Mobileye increase. Additionally, it was determined that in “ideal” conditions, the minimal value of retroreflection for a minimum level 2 detection should be above 55 mcd/lx/m(2) and 88 mcd/lx/m(2) for the best detection quality (level 3). The results of this study are valuable to researchers, road authorities and policymakers

Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator

As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success