Integrating machine learning and model predictive control for automotive applications: A review and future directions
In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
Discriminator-guided model-based offline imitation learning
Offline imitation learning (IL) is a powerful method to solve decision-making problems from
expert demonstrations without reward labels. Existing offline IL methods suffer from severe …
expert demonstrations without reward labels. Existing offline IL methods suffer from severe …
Tofg: Temporal occupancy flow graph for prediction and planning in autonomous driving
In autonomous driving, an accurate understanding of the environment, eg, the vehicle-to-
vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks, such as …
vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks, such as …
Shail: Safety-aware hierarchical adversarial imitation learning for autonomous driving in urban environments
Designing a safe and human-like decision-making system for an autonomous vehicle is a
challenging task. Generative imitation learning is one possible approach for automating …
challenging task. Generative imitation learning is one possible approach for automating …
Human-like decision making for lane change based on the cognitive map and hierarchical reinforcement learning
C Lu, H Lu, D Chen, H Wang, P Li, J Gong - Transportation research part C …, 2023 - Elsevier
Abstract Human-like decision making is crucial to developing an autonomous driving system
(ADS) with high acceptance. Inspired by the cognitive map, this paper proposes a …
(ADS) with high acceptance. Inspired by the cognitive map, this paper proposes a …
Edge-assisted v2x motion planning and power control under channel uncertainty
Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to
achieve safe and efficient autonomous driving, since it leverages the global position …
achieve safe and efficient autonomous driving, since it leverages the global position …
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning
Learning from demonstration (LfD) is a popular technique that uses expert demonstrations to
learn robot control policies. However, the difficulty in acquiring expert-quality demonstrations …
learn robot control policies. However, the difficulty in acquiring expert-quality demonstrations …
A cooperative car-following control model combining deep optical flow estimation and deep reinforcement learning for hybrid electric vehicles
J Zhou, J Chang, A Guo, W Zhao… - Proceedings of the …, 2023 - journals.sagepub.com
Deep reinforcement learning (DRL) based car-following control (CFC) models are widely
applied in the longitudinal motion control tasks of automated vehicles by self-learning for the …
applied in the longitudinal motion control tasks of automated vehicles by self-learning for the …
自动驾驶汽车轨迹规划方法综述.
吕贵林， 高洪伟， 陈涛， 田鹤， 韩爽 - Automotive Digest, 2023 - search.ebscohost.com
作为自动驾驶车辆的重要组成部分, 轨迹规划是影响自动驾驶智能化水平的关键. 综述10
年来智能驾驶车辆轨迹规划方法并将这些算法分为基于图搜索, 基于采样, 基于数学优化 …
年来智能驾驶车辆轨迹规划方法并将这些算法分为基于图搜索, 基于采样, 基于数学优化 …
Deep Learning Algorithms for Smart Cars: A Survey
M Abotaleb, N Bailek - Full Length Article, 2023 - americaspg.com
The rate of progress in autonomous car technology has increased exponentially over the
past decade, mostly thanks to advancements in deep learning and artificial intelligence. This …
past decade, mostly thanks to advancements in deep learning and artificial intelligence. This …