A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving

Nicola Albarella, Dario Giuseppe Lui, Alberto Petrillo, Stefania Santini

Energies 2023 article cited by 23

Source Open-access PDF More in Robotics & Autonomy

importance 6/10 ai assisted Autonomous Vehicle Technology and Safety autonomous vehicles stage: research

Problem

Autonomous vehicles must choose optimal driving decisions in highly variable and unpredictable highway traffic scenarios, which is a challenging planning task.

Why it matters

Safe autonomous highway driving directly affects road safety and the viability of self-driving transport.

What the AI did

A hybrid two-layer architecture combines a Deep Reinforcement Learning high-level planner that learns tactical driving behaviors with model-based approaches for lateral and longitudinal control commands.

Results

not stated in source

Impact

The paper proposes an architecture for highway driving behavior, but the abstract gives no demonstrated outcome.

Record

Venue	Energies
Published	2023
Work type	article
Authors	Nicola Albarella, Dario Giuseppe Lui, Alberto Petrillo, Stefania Santini
Citations	23 (OpenAlex, at import time)
References	60
Model or method	DRL-based high-level planner combined with model-based optimal control
Organization	not stated in source
Primary source	https://doi.org/10.3390/en16083490
Open access	gold
Keywords	autonomous driving, deep reinforcement learning, path planning, highway traffic, optimal control

Importance rating

6 / 10

significant — thousands to millions affected

Autonomous highway driving bears on road safety and transport viability; abstract calls decision-making crucial but states no scale, and evaluation is simulation-only with no demonstrated outcome.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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