A Game-Theoretic Approach to Replanning-Aware Interactive Scene Prediction and Planning

Bahram, Mohammad; Lawitzky, Andreas; Friedrichs, Jasper; Aeberhard, Michael; Wollherr, Dirk

doi:10.1109/tvt.2015.2508009

Cited by 96 publications

(65 citation statements)

References 26 publications

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“…Capturing the mutual dependency of maneuver decisions between different agents, planning can be conducted with foresight [5], [6]. While [5] plans only the next maneuver focusing on the reduction of collision probabilities between all traffic participants, [6] explicitly addresses longer planning horizons and the replanning capabilities of others.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

Wolf

Kurzer

Wingert

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic state representation. This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training. The input for the neural network is a simulated object list similar to that of Radar or Lidar sensors, superimposed by a relational semantic scene description. The state as well as the reward are extended by a behavior adaptation function and a parameterization respectively. With little expert knowledge and a set of mid-level actions, it can be seen that the agent is capable to adhere to traffic rules and learns to drive safely in a variety of situations.

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Section: Related Workmentioning

confidence: 99%

Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

Wolf

Kurzer

Wingert

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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“…Due to the short look ahead in combination with a fixed number of maneuvers, cooperative plans in urban driving scenarios can not correctly be predicted. While [6] extends the approach to longer planning horizons it does not incorporate continuous action spaces, limiting its applicability to highway driving.…”

Section: Related Work a Predictionmentioning

confidence: 99%

“…As MCTS is an anytime algorithm [22], it will always return an estimate. 1) Selection: During the selection phase the UCT (Upper Confidence Bound for Trees) value, see (6), for a state action tuple is calculated, and the successor state with the maximum UCT value is selected. This process repeats itself, until a state is encountered that has not been fully explored (i.e.…”

Section: B Cooperative Planning Algorithmmentioning

confidence: 99%

Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

Kurzer

Engelhorn

Zöllner

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants, and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that the vast amount of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. To accelerate the planning process, we combined learned heuristics with a cooperative planning method in order to guide the search towards regions with promising actions, yielding better results at lower computational costs.

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“…The problem lies in that the artificial rules is not robust and completeness are not guaranteed. At the same time, machine learning based decision methods also have been investigated elaborately [4]- [7]. These methods model the interactions between the agent and other traffic participants in discrete actions and search an optimal action in a tree graph.…”

Section: Introductionmentioning

confidence: 99%

Synchronous Maneuver Searching and Trajectory Planning for Autonomous Vehicles in Dynamic Traffic Environments

Qian¹,

Xu²,

Zeng³

et al. 2022

IEEE Intell. Transport. Syst. Mag.

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In the real-time decision-making and local planning process of autonomous vehicles in dynamic environments, the autonomous driving system may fail to find a reasonable policy or even gets trapped in some situation due to the complexity of global tasks and the incompatibility between upper level maneuver decisions with the lower level trajectory planning. To solve this problem, this paper presents a synchronous maneuver searching and trajectory planning (SMSTP) algorithm based on the topological concept of homotopy. Firstly, a set of alternative maneuvers with boundary limits are enumerated on a multilane road. Instead of sampling numerous paths in the whole spatio-temporal space, we, for the first time, propose using Trajectory Profiles (TPs) to quickly construct the topological maneuvers represented by different routes, and put forward a corridor generation algorithm based on graph-search. The bounded corridor further constrains the maneuver's space in the spatial space. A step-wise heuristic optimization algorithm is then proposed to synchronously generate a feasible trajectory for each maneuver. To achieve real-time performance, we initialize the states to be optimized with the boundary constraints of maneuvers, and we set some heuristic states as terminal targets in the quadratic cost function. The solution of a feasible trajectory is always guaranteed only if a specific maneuver is given. The simulation and realistic driving-test experiments verified that the proposed SMSTP algorithm has a short computation time which is less than 37ms, and the experimental results showed the validity and effectiveness of the SMSTP algorithm.

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A Game-Theoretic Approach to Replanning-Aware Interactive Scene Prediction and Planning

Cited by 96 publications

References 26 publications

Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

Synchronous Maneuver Searching and Trajectory Planning for Autonomous Vehicles in Dynamic Traffic Environments

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