自動駕駛模擬：用於驗證 ADAS 和 ADS 的道路仿真

每年，汽車製造商和供應商都在不斷擴大 ADAS（高級駕駛輔助系統）和 ADS（自動駕駛系統）的範圍，以補充或取代人類駕駛員。其主要目標之一是透過減少碰撞來提高道路安全。然而，駕駛是人類經常執行的高度複雜的任務。光是在美國，人們每年駕駛 3.2 兆英里，但平均每 50 萬英里僅發生一次事故。

驗證 ADAS 和 ADS 實際上比人類駕駛員更安全是一個非常高的障礙，特別是考慮到駕駛環境幾乎無限的變化以及重現測試條件的難度。過去幾十年來，模擬已成為驗證汽車安全系統的重要工具，對於證明 ADAS/ADS 的有效性也至關重要。

ADAS/ADS 開發人員使用開發和測試過程的每一步來驗證新概念，並確保對已證明有效的系統進行更改不會引入錯誤。在各個階段使用各種模擬工具。將軟體、硬體和推動程式合併到環路中的開環和閉環模擬都被廣泛使用。還需要用於產生測試場景的自動化工具，以確保測試套件的足夠覆蓋範圍。大多數模擬工作流程結合了來自多個供應商的各種工具，有助於確保 ADAS 和 ADS 在部署到公共道路上之前有助於提高安全性。

目錄

• 序論
• 背景情況
• 推薦事項
• 由於 ADAS/ADS 的出現，對模擬的需求不斷增加
• 在虛擬空間中再現實體駕駛環境
  • 運算平台
  • 模擬的種類
    • 單元/子系統模擬
    • 全端模擬
    • 循環中的軟體
    • 循環中的硬體
    • 循環中的推動程式
  • 模式和情勢
    • 車輛的物理的建模
    • 感測器建模
    • 景色建模
    • 情勢建立
    • 模式的檢驗
• 安全自動化需要虛擬驗證
  • 收集和分享基礎設施數據
  • 走向監管

With each passing year, automakers and suppliers are continuing to expand the scope of what advanced driver assistance systems (ADAS) and automated driving systems (ADS) can do to supplement or replace human drivers. One of the primary goals is to improve road safety by reducing the number of crashes. However, driving is a very complex task that humans do with a very high frequency. In the US alone, people drive as much as 3.2 trillion miles per year and only crash about once every half million miles on average.

Validating that ADAS and ADS are actually safer than human drivers is a very high bar, particularly given the nearly infinite variability of the driving environment and the difficulty of reproducing test conditions. Simulation has become a crucial tool for validating automotive safety systems over the past several decades, and it is essential for proving the efficacy of ADAS/ADS.

ADAS/ADS developers rely on a range of simulation tools at all stages of the development and testing process to validate new concepts and ensure that changes have not caused errors in systems that are already demonstrated to work. Open- and closed-loop simulations with software, hardware, and drivers in the loop are all being used extensively. Automated tools to generate testing scenarios are also needed to ensure sufficient coverage of the test suite. Most simulation workflows combine a range of tools from multiple vendors to help guarantee that ADAS and ADS contribute to improved safety before the technology is deployed on public roads.

Table of Contents

Spark

Context

Recommendations

The Emergence of ADAS and ADS Drives Simulation Demand

Replicating the Physical Driving Environment in Virtual Space

Compute Platforms

Simulation Types

Unit and Subsystem Simulation

Full-Stack Simulation

Software in the Loop

Hardware in the Loop

Driver in the Loop

Models and Scenarios

Vehicle Physics Modeling

Sensor Modeling

Scene Modeling

Scenario Building

Model Validation

Safe Automation Needs Virtual Validation

Collecting and Sharing Infrastructure Data

Looking toward Regulations

List of Figures

• Annual Light Duty Vehicle Deployments by Automation Level, World Markets: 2022-2031

NVIDIA DRIVE Sim's Virtual Driving Environments and Simulated Sensor Inputs

VI-grade's DiM150 Dynamic Driving Simulator at Ford Product Development Center

Vehicle Physics Simulation Model