This paper describes the methodology for building a dynamic risk assessment
for ADAS (Advanced Driving Assistance Systems) algorithms in parking scenarios,
fusing exterior and interior perception for a better understanding of the scene
and a more comprehensive risk estimation. This includes the definition of a
dynamic risk methodology that depends on the situation from inside and outside
the vehicle, the creation of a multi-sensor dataset of risk assessment for ADAS
benchmarking purposes, and a Local Dynamic Map (LDM) that fuses data from the
exterior and interior of the car to build an LDM-based Dynamic Risk Assessment
System (DRAS).

基于车内外情景融合的动态风险评估方法，创建用于 ADAS 性能评估的多传感器数据集并融合车辆内外数据构建基于 LDM 的动态风险评估系统。

基于 LDM 系统的停车场场景动态风险评估方法

Dynamic Risk Assessment Methodology with an LDM-based System for Parking  Scenarios

As the horizon of intelligent transportation expands with the evolution of
Automated Driving Systems (ADS), ensuring paramount safety becomes more
imperative than ever. Traditional risk assessment methodologies, primarily
crafted for human-driven vehicles, grapple to adequately adapt to the
multifaceted, evolving environments of ADS. This paper introduces a framework
for real-time Dynamic Risk Assessment (DRA) in ADS, harnessing the potency of
Artificial Neural Networks (ANNs).
Our proposed solution transcends these limitations, drawing upon ANNs, a
cornerstone of deep learning, to meticulously analyze and categorize risk
dimensions using real-time On-board Sensor (OBS) data. This learning-centric
approach not only elevates the ADS's situational awareness but also enriches
its understanding of immediate operational contexts. By dissecting OBS data,
the system is empowered to pinpoint its current risk profile, thereby enhancing
safety prospects for onboard passengers and the broader traffic ecosystem.
Through this framework, we chart a direction in risk assessment, bridging the
conventional voids and enhancing the proficiency of ADS. By utilizing ANNs, our
methodology offers a perspective, allowing ADS to adeptly navigate and react to
potential risk factors, ensuring safer and more informed autonomous journeys.

这篇研究论文介绍了一种基于人工神经网络的实时动态风险评估框架，利用车载传感器数据分析和分类风险维度，将传统风险评估方法应用于智能交通系统，以提高自动驾驶系统的安全性能。