Author name cluster

Mingyu Wang 0002

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

ICRA Conference 2022 Conference Paper

Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers

Rohan Chandra
Mingyu Wang 0002
Mac Schwager
Dinesh Manocha

We present a novel approach for risk-aware planning with human agents in multi-agent traffic scenarios. Our approach takes into account the wide range of human driver behaviors on the road, from aggressive maneuvers like speeding and overtaking, to conservative traits like driving slowly and conforming to the right-most lane. In our approach, we learn a mapping from a data-driven human driver behavior model called the CMetric to a driver's entropic risk preference. We then use the derived risk preference within a game-theoretic risk-sensitive planner to model risk-aware interactions among human drivers and an autonomous vehicle in various traffic scenarios. We demonstrate our method in a merging scenario, where our results show that the final trajectories obtained from the risk-aware planner generate desirable emergent behaviors. Particularly, our planner recognizes aggressive human drivers and yields to them while maintaining a greater distance from them. In a user study, participants were able to distinguish between aggressive and conservative simulated drivers based on trajectories generated from our risk-sensitive planner. We also observe that aggressive human driving results in more frequent lane-changing in the planner. Finally, we compare the performance of our modified risk-aware planner with existing methods and show that modeling human driver behavior leads to safer navigation.

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ICRA Conference 2020 Conference Paper

Enhancing Game-Theoretic Autonomous Car Racing Using Control Barrier Functions

Gennaro Notomista
Mingyu Wang 0002
Mac Schwager
Magnus Egerstedt

In this paper, we consider a two-player racing game, where an autonomous ego vehicle has to be controlled to race against an opponent vehicle, which is either autonomous or human-driven. The approach to control the ego vehicle is based on a Sensitivity-ENhanced NAsh equilibrium seeking (SENNA) method, which uses an iterated best response algorithm in order to optimize for a trajectory in a two-car racing game. This method exploits the interactions between the ego and the opponent vehicle that take place through a collision avoidance constraint. This game-theoretic control method hinges on the ego vehicle having an accurate model and correct knowledge of the state of the opponent vehicle. However, when an accurate model for the opponent vehicle is not available, or the estimation of its state is corrupted by noise, the performance of the approach might be compromised. For this reason, we augment the SENNA algorithm by enforcing Permissive RObust SafeTy (PROST) conditions using control barrier functions. The objective is to successfully overtake or to remain in the front of the opponent vehicle, even when the information about the latter is not fully available. The successful synergy between SENNA and PROST-antithetical to the notable rivalry between the two namesake Formula 1 drivers-is demonstrated through extensive simulated experiments.

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IROS Conference 2020 Conference Paper

Game-Theoretic Planning for Risk-Aware Interactive Agents

Mingyu Wang 0002
Negar Mehr
Adrien Gaidon
Mac Schwager

Modeling the stochastic behavior of interacting agents is key for safe motion planning. In this paper, we study the interaction of risk-aware agents in a game-theoretical framework. Under the entropic risk measure, we derive an iterative algorithm for approximating the intractable feedback Nash equilibria of a risk-sensitive dynamic game. We use an iteratively linearized approximation of the system dynamics and a quadratic approximation of the cost function in solving a backward recursion for finding feedback Nash equilibria. In this respect, the algorithm shares a similar structure with DDP and iLQR methods. We conduct experiments in a set of challenging scenarios such as roundabouts. Compared to ignoring the game interaction or the risk sensitivity, we show that our risk-sensitive game-theoretic framework leads to more timeefficient, intuitive, and safe behaviors when facing underlying risks and uncertainty.

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ICRA Conference 2018 Conference Paper

Safe Distributed Lane Change Maneuvers for Multiple Autonomous Vehicles Using Buffered Input Cells

Mingyu Wang 0002
Zijian Wang 0003
Shreyasha Paudel
Mac Schwager

This paper introduces the Buffered Input Cell as a reciprocal collision avoidance method for multiple vehicles with high-order linear dynamics, extending recently proposed methods based on the Buffered Voronoi Cell [1] and generalized Voronoi diagrams [2]. We prove that if each vehicle's control input remains in its Buffered Input Cell at each time step, collisions will be avoided indefinitely. The method is fast, reactive, and only requires that each vehicle measures the relative position of neighboring vehicles. We incorporate this collision avoidance method as one layer of a complete lane change control stack for autonomous cars in a freeway driving scenario. The lane change control stack comprises a decision-making layer, a trajectory planning layer, a trajectory following feedback controller, and the Buffered Input Cell for collision avoidance. We show in simulations that collisions are avoided with multiple vehicles simultaneously changing lanes on a freeway. We also show in simulations that autonomous cars using the BIC method effectively avoid collisions with an aggressive human-driven car.

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