Advanced driver-assistance system

Summary

An advanced driver-assistance system (ADAS) includes technologies that assist drivers with the safe operation of a vehicle. Through a human-machine interface, ADAS increases car and road safety. ADAS uses automated technology, such as sensors and cameras, to detect nearby obstacles or driver errors, and respond accordingly. ADAS can enable various levels of autonomous driving. As most road crashes occur due to human error, ADAS are developed to automate, adapt, and enhance vehicle technology for safety and better driving. ADAS are proven to reduce road fatalities by minimizing human error. Safety features are designed to avoid crashes and collisions by offering technologies that alert the driver to problems, implementing safeguards, and taking control of the vehicle if necessary. Adaptive features may automate lighting, provide adaptive cruise control, assist in avoiding collisions, incorporate satellite navigation and traffic warnings, alert drivers to possible obstacles, assist in

Official source

https://en.wikipedia.org/wiki/Advanced_driver-assistance_system

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Dynamic Vehicle Drifting With Nonlinear MPC and a Fused Kinematic-Dynamic Bicycle Model

Guillaume Denis Antoine Bellegarda

In this letter we present a versatile trajectory optimization framework that leverages a fused kinematic-dynamic bicycle model for highly dynamic vehicle drifting maneuvers. Our framework can be used online to generate drifting maneuvers, offline to plan drift parking, and additionally enables online tracking of the offline computed parking maneuvers. Importantly, neither individual kinematic nor dynamic bicycle models alone can be used straightforwardly in an optimization framework to plan nor execute the presented motions, as the former cannot model drifting, and the latter becomes ill-defined at low speeds. We validate our framework in a Gazebo simulation of the MIT RACECAR, where we show several drifting scenarios such as steady-state drifting with a range of desired yaw rates as well as a dynamic drift parking maneuver under noisy conditions, and video results can be found at https://youtu.be/MF1_fS6CQQs.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Network of automated vehicles: The AutoNet2030 vision

Arnaud de La Fortelle, Jean Grégoire, Ali Marjovi, Alcherio Martinoli

AutoNet2030 – Co-operative Systems in Support of Networked Automated Driving by 2030 – is a European project connecting two domains of intensive research: cooperative systems for Intelligent Transportation Systems and Automated Driving. Given the latest developments in the standardization of vehicular communications, vehicles will soon be wirelessly connected, enabling cooperation among them and with the infrastructure. At the same time, some vehicles will offer very advanced driving assistance systems, ranging from Cooperative Adaptive Cruise Control (C-ACC) to full automation. The research issues addressed in AutoNet2030 are as follows: how can all these vehicles with different capabilities most efficiently cooperate to increase safety and fluidity of the traffic system? What kind of information should be exchanged? Which organization (e.g. centralized or distributed) is the best? The purpose of this paper is to introduce the vision and concepts underlying the AutoNet2030 project and the direction of this ongoing research work.

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Mario Bellino, Jacques Jacot, Sascha Kolski

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