Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908849098719232 |
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| author | Varga, Balint Brand, Thomas Schmitz, Marcus Hashemi, Ehsan |
| author_facet | Varga, Balint Brand, Thomas Schmitz, Marcus Hashemi, Ehsan |
| contents | Autonomous vehicles must negotiate with pedestrians in ways that are both safe and socially compliant. We present an interaction-aware model predictive decision-making (IAMPDM) framework that integrates a gap-acceptance-inspired intention model with MPC to jointly reason about human intent and vehicle control in real time. The pedestrian module produces a continuous crossing-propensity signal - driven by time-to-collision (TTC) with an intention discounting mechanism - that modulates MPC safety terms and minimum-distance constraints. We implement IAMPDM in a projection-based, motion-tracked simulator and compare it against a rule-based intention-aware controller (RBDM) and a conservative non-interactive baseline (NIA). In a human-in-the-decision-loop study with 25 participants, intention-aware methods shortened negotiation and completion time relative to NIA across scenarios, at the expense of tighter TTC/DST margins, with no significant difference between IAMPDM and RBDM except for TTC in one scenario. Results indicate that intention-aware decision-making algorithms reduce pedestrian crossing time and improve subjective ratings of comfort, safety, and trust relative to a non-cooperative decision-making algorithm. We discuss implications for real-world deployment of interaction-aware autonomous vehicles. We detail decision-making calibration and real-time implementation (CasADi/IPOPT) and propose deployment guardrails - minimum surrogate-safety margins, deadlock prevention - to balance efficiency with safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios Varga, Balint Brand, Thomas Schmitz, Marcus Hashemi, Ehsan Systems and Control Robotics 93C95, 90B20, 68T37 I.2.8; I.2.9; H.1.2 Autonomous vehicles must negotiate with pedestrians in ways that are both safe and socially compliant. We present an interaction-aware model predictive decision-making (IAMPDM) framework that integrates a gap-acceptance-inspired intention model with MPC to jointly reason about human intent and vehicle control in real time. The pedestrian module produces a continuous crossing-propensity signal - driven by time-to-collision (TTC) with an intention discounting mechanism - that modulates MPC safety terms and minimum-distance constraints. We implement IAMPDM in a projection-based, motion-tracked simulator and compare it against a rule-based intention-aware controller (RBDM) and a conservative non-interactive baseline (NIA). In a human-in-the-decision-loop study with 25 participants, intention-aware methods shortened negotiation and completion time relative to NIA across scenarios, at the expense of tighter TTC/DST margins, with no significant difference between IAMPDM and RBDM except for TTC in one scenario. Results indicate that intention-aware decision-making algorithms reduce pedestrian crossing time and improve subjective ratings of comfort, safety, and trust relative to a non-cooperative decision-making algorithm. We discuss implications for real-world deployment of interaction-aware autonomous vehicles. We detail decision-making calibration and real-time implementation (CasADi/IPOPT) and propose deployment guardrails - minimum surrogate-safety margins, deadlock prevention - to balance efficiency with safety. |
| title | Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios |
| topic | Systems and Control Robotics 93C95, 90B20, 68T37 I.2.8; I.2.9; H.1.2 |
| url | https://arxiv.org/abs/2503.01852 |