Memory design for autonomous driving systems

August 23, 2020 //By Frank Ferro, Rambus Inc.
Memory design for autonomous driving systems
Frank Ferro, senior director of product marketing for IP Cores at Rambus, explores how GDDR can improve data processing in ADAS.

Introduction

While most industry watchers believe we are still perhaps over a decade away from fully autonomous vehicles becoming common in European cities, vehicle automation is already a hot spot of design activity.

The automotive industry, its regulators and insurers, unite the various technologies for automating vehicles under the banner of Advanced Driver Assistance Systems (ADAS). They categorise system capabilities into six levels, between level 0 (L0), where the vehicle is being driven by a human with no assistance, and level 5 (L5), full autonomy, where no human attention or intervention is required for the vehicle to drive safely.

ADAS L1 capabilities such as cruise control are considered standard specification for many automotive brands. ADAS L2, partial automation, and the initial L3, conditional automation, systems are on the market.  Audi’s 2018 A8 is one of a few vehicles marketed as having L3 ADAS capabilities; on roads with a central barrier, at speeds below 60Km/h, its ‘Traffic Jam Pilot’ mode can take full control.

The journey to full autonomous driving, ADAS L5 is still under way. In this article, we consider the demands of memory subsystems at the higher ADAS levels, exploring the impact of memory technology choices to identify the current design sweet spot between performance and practicality.

Growing ADAS Memory Bandwidth Demands

Above the most basic ADAS levels, vehicles will require multiple sensors to be integrated to enable the timely and accurate decision making required for safe automated driving.  Typically multiple LiDAR, radar and camera sensors at the front, rear, left, and right of the vehicle will be employed to compose a 360-degree view for situational awareness. Both wide-angle sensors for short range detection of objects, and longer range sensors with a narrower view are usually required.

Analysis from just three years ago, estimated that a comprehensive sensor suite comprising radar, LiDAR, cameras, ultrasonic, vehicle motion, satellite navigation and an inertial measurement system, would require a total sensor bandwidth of around


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