314 research outputs found
Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework
With the development of autonomous driving, it is becoming increasingly
common for autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel
on the same roads. Existing single-vehicle planning algorithms on board
struggle to handle sophisticated social interactions in the real world.
Decisions made by these methods are difficult to understand for humans, raising
the risk of crashes and making them unlikely to be applied in practice.
Moreover, vehicle flows produced by open-source traffic simulators suffer from
being overly conservative and lacking behavioral diversity. We propose a
hierarchical multi-vehicle decision-making and planning framework with several
advantages. The framework jointly makes decisions for all vehicles within the
flow and reacts promptly to the dynamic environment through a high-frequency
planning module. The decision module produces interpretable action sequences
that can explicitly communicate self-intent to the surrounding HVs. We also
present the cooperation factor and trajectory weight set, bringing diversity to
autonomous vehicles in traffic at both the social and individual levels. The
superiority of our proposed framework is validated through experiments with
multiple scenarios, and the diverse behaviors in the generated vehicle
trajectories are demonstrated through closed-loop simulations
TrafficMCTS: A Closed-Loop Traffic Flow Generation Framework with Group-Based Monte Carlo Tree Search
Digital twins for intelligent transportation systems are currently attracting
great interests, in which generating realistic, diverse, and human-like traffic
flow in simulations is a formidable challenge. Current approaches often hinge
on predefined driver models, objective optimization, or reliance on
pre-recorded driving datasets, imposing limitations on their scalability,
versatility, and adaptability. In this paper, we introduce TrafficMCTS, an
innovative framework that harnesses the synergy of groupbased Monte Carlo tree
search (MCTS) and Social Value Orientation (SVO) to engender a multifaceted
traffic flow replete with varying driving styles and cooperative tendencies.
Anchored by a closed-loop architecture, our framework enables vehicles to
dynamically adapt to their environment in real time, and ensure feasible
collision-free trajectories. Through comprehensive comparisons with
state-of-the-art methods, we illuminate the advantages of our approach in terms
of computational efficiency, planning success rate, intent completion time, and
diversity metrics. Besides, we simulate highway and roundabout scenarios to
illustrate the effectiveness of the proposed framework and highlight its
ability to induce diverse social behaviors within the traffic flow. Finally, we
validate the scalability of TrafficMCTS by showcasing its prowess in
simultaneously mass vehicles within a sprawling road network, cultivating a
landscape of traffic flow that mirrors the intricacies of human behavior
- …