This paper investigates the high-level decision-making problem in highway
scenarios regarding lane changing and over-taking other slower vehicles. In
particular, this paper aims to improve the Travel Assist feature for automatic
overtaking and lane changes on highways. About 9 million samples including lane
images and other dynamic objects are collected in simulation. This data;
Overtaking on Simulated HighwAys (OSHA) dataset is released to tackle this
challenge. To solve this problem, an architecture called SwapTransformer is
designed and implemented as an imitation learning approach on the OSHA dataset.
Moreover, auxiliary tasks such as future points and car distance network
predictions are proposed to aid the model in better understanding the
surrounding environment. The performance of the proposed solution is compared
with a multi-layer perceptron (MLP) and multi-head self-attention networks as
baselines in a simulation environment. We also demonstrate the performance of
the model with and without auxiliary tasks. All models are evaluated based on
different metrics such as time to finish each lap, number of overtakes, and
speed difference with speed limit. The evaluation shows that the
SwapTransformer model outperforms other models in different traffic densities
in the inference phase.

该研究聚焦于高速公路场景中的高层次决策问题，旨在改善高速公路上自动超车和变道的旅行辅助功能。研究提出了名为 SwapTransformer 的架构作为模仿学习方法，通过辅助任务来帮助模型更好地理解周围环境。实验结果表明，SwapTransformer 模型在不同的交通密度下在推理阶段表现优于其他模型。

SwapTransformer: 基于 OSHA 数据集的模仿学习高速公路超车战术规划模型

SwapTransformer: highway overtaking tactical planner model via imitation  learning on OSHA dataset

Machine learning techniques have outperformed numerous rule-based methods for
decision-making in autonomous vehicles. Despite recent efforts, lane changing
remains a major challenge, due to the complex driving scenarios and changeable
social behaviors of surrounding vehicles. To help improve the state of the art,
we propose to leveraging the emerging \underline{D}eep
\underline{R}einforcement learning (DRL) approach for la\underline{NE} changing
at the \underline{T}actical level. To this end, we present "DRNet", a novel and
highly efficient DRL-based framework that enables a DRL agent to learn to drive
by executing reasonable lane changing on simulated highways with an arbitrary
number of lanes, and considering driving style of surrounding vehicles to make
better decisions. Furthermore, to achieve a safe policy for decision-making,
DRNet incorporates ideas from safety verification, the most important component
of autonomous driving, to ensure that only safe actions are chosen at any time.
The setting of our state representation and reward function enables the trained
agent to take appropriate actions in a real-world-like simulator. Our DRL agent
has the ability to learn the desired task without causing collisions and
outperforms DDQN and other baseline models.

机器学习在自主驾驶车辆的决策制定中优于许多基于规则的方法，我们通过提出一种基于强化学习的框架 'DRNet'，使用深度强化学习 (DRL) 来改进车道切换，并结合安全验证来确保只选择安全动作。

DRNet：基于深度强化学习的自动车道变更决策方法

DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep  Reinforcement Learning

It can be difficult to autonomously produce driver behavior so that it
appears natural to other traffic participants. Through Inverse Reinforcement
Learning (IRL), we can automate this process by learning the underlying reward
function from human demonstrations. We propose a new IRL algorithm that learns
a goal-conditioned spatiotemporal reward function. The resulting costmap is
used by Model Predictive Controllers (MPCs) to perform a task without any
hand-designing or hand-tuning of the cost function. We evaluate our proposed
Goal-conditioned SpatioTemporal Zeroing Maximum Entropy Deep IRL (GSTZ)-MEDIRL
framework together with MPC in the CARLA simulator for autonomous driving, lane
keeping, and lane changing tasks in a challenging dense traffic highway
scenario. Our proposed methods show higher success rates compared to other
baseline methods including behavior cloning, state-of-the-art RL policies, and
MPC with a learning-based behavior prediction model.

本文提出了一种新的 IRL 算法，通过学习人类示范的目标条件下的时空奖励函数，生成适合用于 MPC 的费用地图，以实现自动驾驶、车道保持和车道变换任务，并在 CARLA 模拟器中进行了测试。结果表明，相比于行为克隆、现有 RL 策略和基于学习的行为预测模型的 MPC 等基准方法，我们提出的方法具有更高的成功率。