Description

A focused course, tailored for you

Building Modern Automotive LiDAR Integration and Vehicle AI Architecture (Solid-State LiDAR + Sensor Fusion + AI Stack + UN R155/R156 + ISO 21434 + ASPICE + Engagement)

Build the modern automotive LiDAR integration and vehicle AI architecture in 10 weeks. Solid-state LiDAR + sensor fusion + AI stack + UN R155/R156 + ISO 21434 + ASPICE + engagement.

Automotive Tier-1s and OEMs face LiDAR integration and vehicle AI architecture complexity: solid-state LiDAR selection, sensor fusion, AI stack architecture, UN R155 + R156 + ISO 21434 + ASPICE compliance, and customer engagement. Engineers who build the integrated capability take the senior platform work. Here is the 10-week build.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Automotive Tier-1s and OEMs (Bosch, Continental, Denso, ZF Friedrichshafen, Aptiv, Magna International, Forvia, Marelli, Valeo, Hyundai Mobis, Aisin, Faurecia legacy, Plastic Omnium legacy, Knorr-Bremse, Mitsubishi Electric, Hella, Stoneridge, Continental Automotive, AutoLiv, Tesla, Volkswagen Group, BMW Group, Mercedes-Benz, Stellantis, Ford, GM, Honda, Toyota, Hyundai-Kia, Nissan, Renault, Volvo Cars, Mazda, Subaru, Suzuki, Mitsubishi Motors, SAIC, Geely, BYD, NIO, Xpeng, Li Auto, Leapmotor, Lucid, Rivian, Polestar, Lotus, Ferrari, Lamborghini, Bentley, Rolls-Royce, McLaren, Aston Martin) face LiDAR integration and vehicle AI architecture complexity in 2024-2026.

Solid-state LiDAR selection (Innoviz, Aeva, Luminar, Hesai, RoboSense, Cepton, Ouster, Velodyne, Quanergy, Voyant Photonics, Lumotive, in-house), 4D imaging radar selection (Arbe, Smartmicro, Continental, Bosch, ZF, Aptiv, Vayyar, Echodyne, Provizio, Plus.ai, in-house), camera-and-image-sensor selection (Sony Semiconductor, OmniVision, ON Semiconductor, Mobileye EyeQ, NVIDIA DRIVE, Qualcomm Snapdragon Ride, Ambarella CV-AI, Black Sesame, Horizon Robotics, in-house), AI stack architecture (NVIDIA DRIVE Thor, NVIDIA DRIVE Orin, Qualcomm Snapdragon Ride Flex, Mobileye EyeQ6 Lite/High, Tesla FSD chip, in-house ASIC), sensor fusion framework (early-fusion vs mid-fusion vs late-fusion), perception-stack framework, prediction-stack framework, planning-and-control framework, V2X integration framework, UN R155 cybersecurity management system, UN R156 software update management system, ISO 21434 cybersecurity engineering, ASPICE process compliance, ISO 26262 functional safety, ISO/PAS 21448 SOTIF, ISO/IEC TR 24028 AI trustworthiness, EU GSR (General Safety Regulation) compliance, USDOT NHTSA compliance, China GB 44495 standards, India BIS AIS-189, and engagement economics for Tier-1 and OEM customers all need to land at the engineering layer.

Engineers who build the integrated capability take the senior platform work. Engineers who stay on classic single-sensor patterns watch the senior work shift to peers.

This course teaches the 10-week build of modern automotive LiDAR integration and vehicle AI architecture: LiDAR framework, sensor fusion framework, AI stack framework, UN R155/R156 framework, ISO 21434 framework, ASPICE framework, customer engagement framework, and the executive engagement model. Twelve modules with deliverables. Plus a hand-built implementation playbook for your specific platform.

What you walk away with

A documented LiDAR framework.
A sensor fusion framework.
An AI stack framework.
A UN R155/R156 framework.
An ISO 21434 framework.
An ASPICE framework.
A customer engagement framework.
An executive engagement model.
A 10-week build plan.

The 12 modules

Module 1. Automotive LiDAR + vehicle AI landscape 2026

Detailed walkthrough of the automotive LiDAR + vehicle AI landscape in 2026: solid-state LiDAR vendor positioning (Innoviz, Aeva, Luminar, Hesai, RoboSense, Cepton, Ouster, Velodyne, Quanergy, Voyant Photonics, Lumotive), 4D imaging radar landscape (Arbe, Smartmicro, Continental, Bosch, ZF, Aptiv, Vayyar, Echodyne, Provizio), camera-and-image-sensor landscape (Sony Semiconductor, OmniVision, ON Semiconductor, Mobileye EyeQ, NVIDIA DRIVE, Qualcomm Snapdragon Ride, Ambarella CV-AI, Black Sesame, Horizon Robotics), AI stack landscape (NVIDIA DRIVE Thor, NVIDIA DRIVE Orin, Qualcomm Snapdragon Ride Flex, Mobileye EyeQ6 Lite/High, Tesla FSD chip), regulatory landscape (UN R155, UN R156, ISO 21434, ASPICE, ISO 26262, ISO/PAS 21448 SOTIF, ISO/IEC TR 24028, EU GSR, USDOT NHTSA, China GB 44495, India BIS AIS-189), and the strategic-level decisions facing engineers.

Module 2. LiDAR framework

Build the LiDAR framework: LiDAR vendor selection criteria framework, solid-state vs mechanical-scanning framework, MEMS vs flash vs OPA (Optical Phased Array) vs FMCW framework, range vs resolution vs FOV vs frame rate framework, eye-safety framework (Class 1 vs Class 1M), automotive grade framework (AEC-Q102), integration-into-vehicle framework (windshield-integrated, bumper-integrated, roof-integrated, headlight-integrated), thermal management framework, and the integration with broader sensor strategy.

Module 3. Sensor fusion framework

Build the sensor fusion framework: early-fusion vs mid-fusion vs late-fusion framework, time-synchronisation framework (PTP, GPS, in-house), spatial-calibration framework, sensor-fusion ML model framework (CNN-based, Transformer-based, BEV-Transformer-based, occupancy-network-based), uncertainty-quantification framework, multi-modal-fusion architecture framework, and the integration with broader perception.

Module 4. AI stack framework

Build the AI stack framework: SoC selection framework (NVIDIA DRIVE Thor, NVIDIA DRIVE Orin, Qualcomm Snapdragon Ride Flex, Mobileye EyeQ6 Lite/High, Tesla FSD chip, in-house ASIC), AI compute partitioning framework (perception, prediction, planning, monitoring), AI workload orchestration framework, AI runtime framework (TensorRT, OpenVINO, ONNX Runtime, in-house), model-compression framework (quantisation, pruning, distillation), AI safety monitor framework, AI fail-safe framework, and the integration with broader vehicle architecture.

Module 5. UN R155/R156 framework

Build the UN R155/R156 framework: UN R155 CSMS (Cybersecurity Management System) framework, UN R156 SUMS (Software Update Management System) framework, threat-analysis framework, risk-assessment framework (TARA), security-controls framework, software-update process framework, security-monitoring framework, security-incident response framework, supplier-management framework, and the integration with broader regulatory affairs.

Module 6. ISO 21434 framework

Build the ISO 21434 framework: cybersecurity engineering organisation framework, project-dependent cybersecurity management framework, distributed cybersecurity activities framework, continuous cybersecurity activities framework, concept-phase cybersecurity framework, product-development cybersecurity framework, production cybersecurity framework, operations and maintenance cybersecurity framework, end-of-cybersecurity-support framework, and the integration with broader product development.

Module 7. ASPICE framework

Build the ASPICE framework: process-assessment-model framework, capability-level framework (Level 0 through Level 5), system-engineering process group framework, software-engineering process group framework, supporting processes framework, management processes framework, organisational processes framework, AS-PICE for cybersecurity framework, AS-PICE for AI/ML framework, and the integration with broader process improvement.

Module 8. ISO 26262 and SOTIF framework

Build the ISO 26262 and SOTIF framework: ISO 26262 functional safety framework, ASIL determination framework, safety-goal framework, safety-requirement framework, safety-mechanism framework, safety-case framework, ISO/PAS 21448 SOTIF framework, performance-limitations framework, triggering-conditions framework, and the integration with broader safety engineering.

Module 9. V2X integration framework

Build the V2X integration framework: DSRC vs C-V2X framework, 4G-V2X vs 5G-V2X framework, edge-cloud integration framework, road-side-unit integration framework, traffic-management-system integration framework, emergency-vehicle priority framework, V2X security framework, and the integration with broader connectivity.

Module 10. Cross-jurisdiction regulatory framework

Build the cross-jurisdiction regulatory framework: EU GSR + UN R157 ALKS framework, USDOT NHTSA framework, China GB 44495 framework, India BIS AIS-189 framework, Japan MLIT framework, Korea KMVSS framework, Australia ADR framework, Singapore framework, Brazil INMETRO framework, and the integration with broader market access.

Module 11. Customer engagement framework

Build the customer engagement framework: OEM-Tier1 engagement framework, OEM-platform-architecture engagement framework, OEM-procurement engagement framework, design-win pursuit framework, FAE (Field Applications Engineer) deployment framework, and the integration with broader customer engagement.

Module 12. Your 10-week build plan

Week-by-week plan with weekly deliverables. Weeks 1-2: automotive LiDAR + vehicle AI landscape + LiDAR framework. Weeks 3-4: sensor fusion framework + AI stack framework. Weeks 5-6: UN R155/R156 framework + ISO 21434 framework. Weeks 7-8: ASPICE framework + ISO 26262 and SOTIF framework. Weeks 9-10: V2X integration framework + cross-jurisdiction regulatory framework + customer engagement framework. Deliverable: modern automotive LiDAR integration and vehicle AI architecture.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers the landscape.

Module 2 produces the LiDAR framework.

Module 3 covers sensor fusion.

Module 4 covers AI stack.

Module 5 covers UN R155/R156.

Module 6 covers ISO 21434.

Module 7 covers ASPICE.

Module 8 covers ISO 26262 and SOTIF.

Module 9 covers V2X integration.

Module 10 covers cross-jurisdiction regulatory.

Module 11 covers customer engagement.

Module 12 covers the 10-week build plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates and design references for LiDAR framework, sensor fusion framework, AI stack framework, UN R155/R156 framework, ISO 21434 framework, ASPICE framework, ISO 26262 and SOTIF framework, V2X integration framework, cross-jurisdiction regulatory framework, customer engagement framework.
A hand-built implementation playbook generated for your specific platform.
Three worked examples of modern automotive LiDAR integration and vehicle AI architectures at peer Tier-1s and OEMs.
Scripted talking points for the customer CTO and platform-architect engagement.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: LiDAR framework scaffold drafted.

Week 4: Sensor fusion + AI stack designed.

Week 8: UN R155/R156 + ISO 21434 + ASPICE + ISO 26262 + SOTIF operational.

Week 10: Architecture in operation.

Before and after

Before

Your platform ships single-sensor or limited-multi-sensor architectures. LiDAR integration is reactive. AI stack architecture lags peer platforms. UN R155 + R156 + ISO 21434 + ASPICE compliance overhead grows. Senior platform work goes to peers shipping the integrated capability.

After

A modern automotive LiDAR integration and vehicle AI architecture is in operation. LiDAR framework, sensor fusion framework, AI stack framework, UN R155/R156 framework, ISO 21434 framework, ASPICE framework, ISO 26262 and SOTIF framework, V2X integration framework, cross-jurisdiction regulatory framework, customer engagement framework are all designed.

What happens if you do not address this

Engineers without the integrated capability miss senior platform work. UN R155 + R156 effective July 2024 for new types; EU GSR active for new types; ISO 21434 the cybersecurity baseline.

Who it is for

For automotive embedded engineers, AI/ML engineers, perception engineers, sensor-fusion engineers, cybersecurity engineers, and senior engineering managers at Tier-1s and OEMs.

Who this is NOT for. Pure aftermarket-automotive consultants without OEM scope. Engineers at firms with no automotive business. Pure non-automotive AI engineers.

How it arrives

Text-based course via LMS, plus downloadable design references and templates and the hand-built implementation playbook.

Time investment. Roughly 18 hours of reading and 200 to 400 hours of engineering effort across the 10-week build.

Why $199 is the right number

External automotive LiDAR + AI consultants (Big4 automotive practices, McKinsey Automotive, BCG Automotive, Bain Automotive, the firm Automotive, the firm Automotive, IBM Consulting Automotive, the firm Automotive, the firm Automotive, the firm Automotive, the firm Automotive, specialist firms like ETAS, Vector Informatik, dSPACE, AVL List, FEV, IAV, Bertrandt, EDAG, MBtech) charge $500K-$3M for LiDAR + AI architecture programmes. $199 buys the focused playbook plus the implementation document for your specific platform.

FAQ

Will this replace hiring an automotive AI/LiDAR specialist?

Partially. It teaches the integrated capability. You may still want specialist input for advanced 4D imaging radar.

What if my platform is primarily commercial-vehicle (not passenger)?

Modules 2 and 11 cover commercial-vehicle-anchored patterns.

Does this cover ADAS Level 2+ specifically?

Modules 3 and 4 cover ADAS Level 2+ in depth.

What about Level 4 robotaxi platforms?

Modules 3, 4, and 9 cover Level 4 robotaxi patterns.

What is in the implementation playbook for me specifically?

LiDAR framework tailored to your specific platform; AI stack matched to your target SoC; a 10-week build plan.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.