The continual evolution of autonomous driving technology requires
car-following models that can adapt to diverse and dynamic traffic
environments. Traditional learning-based models often suffer from performance
degradation when encountering unseen traffic patterns due to a lack of
continual learning capabilities. This paper proposes a novel car-following
model based on continual learning that addresses this limitation. Our framework
incorporates Elastic Weight Consolidation (EWC) and Memory Aware Synapses (MAS)
techniques to mitigate catastrophic forgetting and enable the model to learn
incrementally from new traffic data streams. We evaluate the performance of the
proposed model on the Waymo and Lyft datasets which encompass various traffic
scenarios. The results demonstrate that the continual learning techniques
significantly outperform the baseline model, achieving 0\% collision rates
across all traffic conditions. This research contributes to the advancement of
autonomous driving technology by fostering the development of more robust and
adaptable car-following models.

本文基于连续学习提出了一种新颖的车辆跟驰模型，通过采用弹性权重合并（EWC）和记忆感知神经元（MAS）技术，实现了针对多样化和动态交通环境的自适应能力，这些连续学习技术在 Waymo 和 Lyft 数据集上的实验结果显示出显著的性能优势，能在各种交通条件下实现 0％碰撞率，为自动驾驶技术的发展做出了重要贡献。

动态交通环境中的适应性车辆跟随的持续学习

Continual Learning for Adaptable Car-Following in Dynamic Traffic  Environments

We present ChatScene, a Large Language Model (LLM)-based agent that leverages
the capabilities of LLMs to generate safety-critical scenarios for autonomous
vehicles. Given unstructured language instructions, the agent first generates
textually described traffic scenarios using LLMs. These scenario descriptions
are subsequently broken down into several sub-descriptions for specified
details such as behaviors and locations of vehicles. The agent then
distinctively transforms the textually described sub-scenarios into
domain-specific languages, which then generate actual code for prediction and
control in simulators, facilitating the creation of diverse and complex
scenarios within the CARLA simulation environment. A key part of our agent is a
comprehensive knowledge retrieval component, which efficiently translates
specific textual descriptions into corresponding domain-specific code snippets
by training a knowledge database containing the scenario description and code
pairs. Extensive experimental results underscore the efficacy of ChatScene in
improving the safety of autonomous vehicles. For instance, the scenarios
generated by ChatScene show a 15% increase in collision rates compared to
state-of-the-art baselines when tested against different reinforcement
learning-based ego vehicles. Furthermore, we show that by using our generated
safety-critical scenarios to fine-tune different RL-based autonomous driving
models, they can achieve a 9% reduction in collision rates, surpassing current
SOTA methods. ChatScene effectively bridges the gap between textual
descriptions of traffic scenarios and practical CARLA simulations, providing a
unified way to conveniently generate safety-critical scenarios for safety
testing and improvement for AVs.

ChatScene 使用大型语言模型通过文本指令生成安全关键场景，将其转化为特定代码以实现车辆预测和控制，并验证了其在提高自动驾驶车辆安全性方面的有效性。

ChatScene：用于自动驾驶车辆的基于知识的安全关键场景生成

ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for  Autonomous Vehicles

Black spot identification, a spatiotemporal phenomenon, involves analyzing
the geographical location and time-based occurrence of road accidents.
Typically, this analysis examines specific locations on road networks during
set time periods to pinpoint areas with a higher concentration of accidents,
known as black spots. By evaluating these problem areas, researchers can
uncover the underlying causes and reasons for increased collision rates, such
as road design, traffic volume, driver behavior, weather, and infrastructure.
However, challenges in identifying black spots include limited data
availability, data quality, and assessing contributing factors. Additionally,
evolving road design, infrastructure, and vehicle safety technology can affect
black spot analysis and determination. This study focused on traffic accidents
in Greek road networks to recognize black spots, utilizing data from police and
government-issued car crash reports. The study produced a publicly available
dataset called Black Spots of North Greece (BSNG) and a highly accurate
identification method.

本文介绍了黑点识别这一空间时间现象，并说明了该分析可以通过特定地点和时间段内道路碰撞的高发集中区域（即称为黑点）来确定交通事故发生的根本原因。此研究关注希腊道路网络上的交通事故，旨在识别黑点，利用警方和政府发布的车祸报告数据，生产了一个公开可用的数据集，即北希腊的黑点（BSNG），以及高度准确的识别方法。