Technology Category
Natural Language Processing: Safety and RobustnessComputer Vision: Vision for Robotics & Autonomous DrivingIntelligent Robots: Multimodal Perception & Sensor Fusion
One Stack, Diverse Vehicles: Checking Safe Portability of Automated Driving Software
🤖 AI Summary
Hardware heterogeneity across vehicle platforms introduces functional safety risks when deploying autonomous driving software. Method: This paper proposes a formal safety portability verification framework that constructs a multi-objective vehicle configuration model—abstracting sensors, actuators, and compute platforms—to automatically derive safety-critical behavior sets and perform controller realizability checks. Contribution/Results: It pioneers the application of formal portability verification to autonomous driving controller adaptation assessment, uniformly supporting safety analysis for both conventional controllers and neural network–based controllers. Evaluated on real-vehicle configurations, the framework enables minute-scale automated verification, precisely identifies adaptation bottlenecks, and generates actionable controller parameter tuning recommendations—thereby significantly accelerating software updates and integration testing.
Technology Category
Application Category
📝 Abstract
Integrating an automated driving software stack into vehicles with variable configuration is challenging, especially due to different hardware characteristics. Further, to provide software updates to a vehicle fleet in the field, the functional safety of every affected configuration has to be ensured. These additional demands for dependability and the increasing hardware diversity in automated driving make rigorous automatic analysis essential. This paper addresses this challenge by using formal portability checking of adaptive cruise controller code for different vehicle configurations. Given a formal specification of the safe behavior, models of target configurations are derived, which capture relevant effects of sensors, actuators and computing platforms. A corresponding safe set is obtained and used to check if the desired behavior is achievable on all targets. In a case study, portability checking of a traditional and a neural network controller are performed automatically within minutes for each vehicle hardware configuration. The check provides feedback for necessary adaptations of the controllers, thus, allowing rapid integration and testing of software or parameter changes.
Problem
Research questions and friction points this paper is trying to address.
Autonomous Driving
Software Adaptability
Hardware Variability
Innovation
Methods, ideas, or system contributions that make the work stand out.
Autonomous Driving Safety
Adaptive Cruise Control
Software Adaptability Testing
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