The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remarkable success in computer vision and natural language processing, robotics remains constrained by limited data, complex real-world interacti...
Mattia Piccinini, L. Schulze, Alice Plebe et al.· 0 citations
Autonomous racecars operate at the limits of vehicle dynamics, where small control errors translate into safety-critical behavior and lost performance. Trajectory planners assume perfect tracking and remain blind to execution errors. To guarantee safety, trajectory planners therefore restrict themselves to conservative...
Ann-Kathrin Schwehn, Alexander Langmann, Mattia Piccinini et al.· 0 citations
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains a fragmented modular pipeline in which scenario generation, retrieval, modification, ADS execution, and results analysis are performed by s...
Yuan Gao, Sebastian Müller, Mattia Piccinini et al.· 0 citations
Solver-Gradient Guided Reinforcement Learning is proposed, a solver-sensitivity augmentation for RL-based online MPC cost-weight adaptation that reaches PPO's best closed-loop return with up to 70.6% fewer samples, and outperforms GB-PL baselines by at least 54% in closed-loop return.
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann et al.· 0 citations
Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatically generating such scripts from regulatory descriptions remains an open challenge, and existing approaches face fundamental trade-offs. Re...
Yuan Gao, Wenting Miao, Mattia Piccinini et al.· 0 citations
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