From ADAS to Autonomous Cars: Key Design Lessons
Kurt Shuler, VP Marketing, Arteris
3/27/2018 00:01 AM EDT
Autonomous driving can be challenging. But here are three major lessons that automotive developers have learned while streamlining the ADAS designs during the past few years.
Autonomous driving systems are challenging design engineers in ways that personal computer, smartphone, and data center systems did not. At the same time, however, there is a lot that semiconductor developers can learn from the evolution of advanced driving assistance systems (ADAS).
So, while integration challenges may perplex the developers of system-on-chips (SoCs) for self-driving vehicles, the ADAS learning curve can be crucial in putting the technology of the century to work in the cars of the future.
Below are three major lessons that automotive developers have learned while streamlining the ADAS designs during the past few years.
E-mail This Article | Printer-Friendly Page |
|
Arteris Hot IP
Related Articles
- Key considerations and challenges when choosing LDOs
- Artificial Intelligence (AI) utilizing deep learning techniques to enhance ADAS
- Mastering Key Technologies to Realize the Dream - M31 IP Integration Services
- Driving ADAS Applications with MIPI CSI-2
- Implementation basics for autonomous driving vehicles
New Articles
- Accelerating RISC-V development with Tessent UltraSight-V
- Automotive Ethernet Security Using MACsec
- What is JESD204C? A quick glance at the standard
- Optimizing Power Efficiency in SOC with PVT Sensor-Assisted DVFS Technology
- Bandgap Reference (BGR) Circuit Design and Transient Analysis in 90nm VLSI Technology
Most Popular
- Accelerating RISC-V development with Tessent UltraSight-V
- System Verilog Assertions Simplified
- Synthesis Methodology & Netlist Qualification
- System Verilog Macro: A Powerful Feature for Design Verification Projects
- Enhancing VLSI Design Efficiency: Tackling Congestion and Shorts with Practical Approaches and PnR Tool (ICC2)