PTLA: Engineering Solutions for Innovation

Overview: Conducted feasibility analysis for Automated Emergency Steering (AES) sensor configuration, assessing impacts on vehicle architecture and dynamics for passenger and commercial vehicles.

Challenges: Managing trailer dynamics for commercial vehicles, ensuring scalability across low- and high-end architectures, and selecting minimally complex sensor technologies.

Solution: Developed a digital twin of AES in a virtual environment, evaluated physics-based sensor models, implemented control algorithms, and analyzed corner cases, including vehicle conditions.

Outcome: Defined optimal sensor configurations, ensured scalable AES solutions, and identified low-complexity sensor suites for robust performance.

Overview: Designed an active safety system for off-road vehicles to enhance detection and protection of vulnerable site users, adaptable to diverse vehicle types.

Challenges: Optimizing sensor placement for timely detection, adapting configurations to vehicle characteristics (e.g., excavator vs. dump truck), and ensuring scalability across platforms.

Solution: Strategically positioned sensors for effective detection, tailored configurations to vehicle types, reused control algorithms across platforms, and simulated impacts of vehicle changes using existing models.

Outcome: Delivered a scalable safety system with warning and emergency intervention features, achieving high reusability and adaptability across vehicle platforms.

Overview: Developed an automated parking system for buses at bus stop traffic signs, integrating lane detection, traffic sign recognition, and longitudinal/lateral control.

Challenges: Positioning sensors for timely detection of all traffic participants, adapting configurations to vehicle class, and ensuring scalability across platforms.

Solution: Optimized sensor placement for effective detection, tailored configurations to bus characteristics, reused control algorithms across vehicle platforms, and simulated impacts of vehicle changes using existing models.

Outcome: Delivered a scalable, reusable automated parking system with robust detection and control, adaptable to various bus classes.

Overview: Conducted a SOTIF study for the resume driving operation of a commercial vehicle’s Adaptive Cruise Control (ACC) Stop & Go system, ensuring safety post-stoppage.

Challenges: Detecting Vulnerable Road Users (VRUs) in front blind spots, optimizing sensor placement on heavy trucks, and confirming driver presence before resuming drive.

Solution: Developed a sensor configuration strategy, implemented VRU detection mechanisms, and integrated driver presence verification before drive resumption.

Outcome: Successfully delivered a robust sensor strategy and detection mechanisms, ensuring safe resumption by confirming driver presence and VRU absence.