AI-Based Autonomous Driving
Identifying and prioritizing sensor-fusion hardware-software stacks capable of meeting SAE Level 4 performance with a clear route to multi-region certification.
Client
Tier-One Automotive Supplier
Objective
Shortlist Certifiable Level 4 Solutions
Timeline
10-Week Sprint
Key Focus
Edge-Case Perception & Robustness
The Challenge: Four Interlocking Hurdles to True Autonomy
To reach true Level 4 autonomy, AI software must fuse data from LiDAR, radar, and cameras in real time. The pathway to this goal is hindered by four interlocking challenges.
Rare events, like unusual road debris or pedestrians in costume, can confound neural networks trained on standard datasets.
Spoofed road signs, LiDAR interference, or sensor glare can trick algorithms into making unsafe decisions.
Varying certification rules across North America, Europe, and Asia mean proving safety in one market doesn't guarantee global approval.
Any headline-grabbing mishap erodes public trust, slowing adoption and inviting tighter regulations.
Key Outcomes: Five Lead Platforms for Level 4 Performance
Our 5-phase sprint, including simulation and attack testing, delivered five leading platforms that achieve ≥99.99% perception accuracy and withstand adversarial attacks.
Strategic Impact
The supplier selected the dual-path compute stack and simulation engine for its next reference platform. The projected 2028 launch will deliver SAE Level 4 autonomy in geo-fenced urban zones, backed by a multi-jurisdiction homologation dossier and built-in defenses against sensor spoofing—setting a new benchmark for safety, performance, and public trust.