This study proposes a framework for human-like autonomous car-following planning based
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …

A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior,
is developed. Its functions include adaptive cruise control and forward collision …

Car following models have been studied with many diverse approaches for decades.
Nowadays, technological advances have significantly improved our traffic data collection …

Adaptive Cruise Control systems have been developed and introduced into the consumer
market for over a decade. Among these systems, fully-adaptive ones are required to adapt …

Eco‐driving assistance systems incorporating predictive or feedforward information are a
promising technique to increase energy‐efficiency and reduce CO 2 emissions from road …

This article deals with the application of artificial neural networks to model car following
drivers' behavior. The study is based on experimental data collected by several global …

Driving is a highly complex task that involves the execution of multiple cognitive tasks
belonging to different levels of abstraction. Traffic emerges from the interaction of a big …

This study aims to develop an optimal cruise controller that can automatically adapt to
individual car-following style. First, the adaptive cruise control (ACC) problem is formulated …

Building a human-like car-following model that can accurately simulate drivers' car-following
behaviors is helpful to the development of driving assistance systems and autonomous …

In this paper a car-following model is formulated as a time-continuous dynamic process,
depending on two parameters and two inputs. One of these inputs is the follower's desired …