Hierarchical Game-Theoretic Planning for Autonomous Vehicles

Fisac, Jaime F.; Bronstein, Eli; Stefansson, Elis; Sadigh, Dorsa; Sastry, S. Shankar; Dragan, Anca D.

doi:10.48550/arxiv.1810.05766

Cited by 2 publications

(4 citation statements)

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“…Game-theoretic planning: Traditionally, multi-agent planning and game theory approaches explicitly model multiple agents' policies or internal states, usually by generalizing Markov decision process (MDP) to multiple decisions makers [5,33]. These frameworks facilitate reasoning about collaboration strategies, but suffer from "state space explosion" intractability except when interactions are known to be sparse [24] or hierarchically decomposable [11]. Multi-agent Forecasting: Data-driven approaches have been applied to forecast complex interactions between multiple pedestrians [1,3,10,14,21], vehicles [6,19,26], and athletes [9,18,20,32,34,35].…”

Section: Related Workmentioning

confidence: 99%

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PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

Rhinehart

McAllister

Kitani

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

334

236

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For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information. Towards these capabilities, we present a probabilistic forecasting model of future interactions of multiple agents. We perform both standard forecasting and conditional forecasting with respect to the AV's goals. Conditional forecasting reasons about how all agents will likely respond to specific decisions of a controlled agent. We train our model on real and simulated data to forecast vehicle trajectories given past positions and LIDAR. Our evaluation shows that our model is substantially more accurate in multi-agent driving scenarios compared to existing state-of-the-art. Beyond its general ability to perform conditional forecasting queries, we show that our model's predictions of all agents improve when conditioned on knowledge of the AV's intentions, further illustrating its capability to model agent interactions.

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Section: Related Workmentioning

confidence: 99%

“…To execute planning, we perform gradient ascent to approximately solve the optimization problem (11). Recall the latent joint behavior is Z .…”

Section: A Planning and Forecasting Algorithmsmentioning

confidence: 99%

PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

Rhinehart

McAllister

Kitani

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

334

236

View full text Add to dashboard Cite

show abstract

“…Lemma 2: For all t ∈ T and any given P n,l,t , Ṽn,l,t (P n,l,t ) = V l,t (P n,l,t ). Moreover, an arbitrary policy {Q n,l,t } t∈T ∈ Q is an optimal solution to (5).…”

Section: Definitionmentioning

confidence: 99%

“…A traffic system can be analyzed by the theory of dynamic games, where the non-cooperative drivers compete over the shared network [4], [5]. Strategic behaviour of individuals often results in a game theoretic equilibrium that is not necessarily socially optimal.…”

Section: Introductionmentioning

confidence: 99%

Linearly-Solvable Mean-Field Approximation for Multi-Team Road Traffic Games

Pedram

Tanaka

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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We study the traffic routing game among a large number of selfish drivers over a traffic network. We consider a specific scenario where the strategic drivers can be classified into teams, where drivers in the same team have identical payoff functions. An incentive mechanism is considered to mitigate congestion, where each driver is subject to dynamic tax penalties. We explore a special case in which the tax is affine in the logarithm of the number of drivers selecting the same route from each team. It is shown via a mean-field approximation that a Nash equilibrium in the limit of a large population can be found by linearly solvable algorithms.

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Hierarchical Game-Theoretic Planning for Autonomous Vehicles

Cited by 2 publications

References 0 publications

PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

Linearly-Solvable Mean-Field Approximation for Multi-Team Road Traffic Games

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