# Intention-Aware Decision-Making for Mixed Intersection Scenarios

arXiv: https://arxiv.org/abs/2303.17493 · HTML: https://mlsanity.com/paper/2303.17493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus](https://mlsanity.com/paper/1902.00487/) (2019)
- [Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction](https://mlsanity.com/paper/1910.09077/) (2019)
- [Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload](https://mlsanity.com/paper/1905.08878/) (2019)
- [Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM](https://mlsanity.com/paper/2007.00113/) (2020)
- [Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network](https://mlsanity.com/paper/2010.13407/) (2020)
- [Intend-Wait-Cross: Towards Modeling Realistic Pedestrian Crossing Behavior](https://mlsanity.com/paper/2203.07324/) (2022)
- [Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model](https://mlsanity.com/paper/2003.09998/) (2020)
- [Limited Information Shared Control: A Potential Game Approach](https://mlsanity.com/paper/2201.06651/) (2022)

## Built on by (most cited first, 1 of 1)

- [Agentic Proposing: Enhancing Large Language Model Reasoning via Compositional Skill Synthesis](https://mlsanity.com/paper/2602.03279/) (2026)

## Abstract (arXiv)

This paper presents a white-box intention-aware decision-making for the handling of interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street crossing scenario. Moreover, a design framework has been developed, which enables automated parameterization of the decision-making. This decision-making is designed in such a manner that it can understand pedestrians in urban traffic and can react accordingly to their intentions. That way, a human-like response to the actions of the pedestrian is ensured, leading to a higher acceptance of AVs. The core notion of this paper is that the intention prediction of the pedestrian to cross the street and decision-making are divided into two subsystems. On the one hand, the intention detection is a data-driven, black-box model. Thus, it can model the complex behavior of the pedestrians. On the other hand, the decision-making is a white-box model to ensure traceability and to enable a rapid verification and validation of AVs. This white-box decision-making provides human-like behavior and a guaranteed prevention of deadlocks. An additional benefit is that the proposed decision-making requires low computational resources only enabling real world usage. The automated parameterization uses a particle swarm optimization and compares two different models of the pedestrian: The social force model and the Markov decision process model. Consequently, a rapid design of the decision-making is possible and different pedestrian behaviors can be taken into account. The results reinforce the applicability of the proposed intention-aware decision-making.