The combinatorial aspect of motion planning: Maneuver variants in structured environments

Bender, Philipp; Taş, Ömer Şahin; Ziegler, Julius; Stiller, Christoph

doi:10.1109/ivs.2015.7225909

Cited by 67 publications

(61 citation statements)

References 15 publications

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“…These are, therefore, already used in safety-critical applications, such as autonomous driving. As shown by Bender et al [17] all combinatorial options (homotopy classes) have to be evaluated in order to find the global optimum when using local optimization. Therefore, these methods generally do not scale well with the increasing complexity of the environment and become infeasible for real-time applications.…”

Section: Related Workmentioning

confidence: 99%

Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization

Hart

Rychly

Knoll

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Many current behavior generation methods struggle to handle real-world traffic situations as they do not scale well with complexity. However, behaviors can be learned off-line using data-driven approaches. Especially, reinforcement learning is promising as it implicitly learns how to behave utilizing collected experiences. In this work, we combine policy-based reinforcement learning with local optimization to foster and synthesize the best of the two methodologies. The policy-based reinforcement learning algorithm provides an initial solution and guiding reference for the post-optimization. Therefore, the optimizer only has to compute a single homotopy class, e.g. drive behind or in front of the other vehicle. By storing the state-history during reinforcement learning, it can be used for constraint checking and the optimizer can account for interactions. The post-optimization additionally acts as a safetylayer and the novel method, thus, can be applied in safetycritical applications. We evaluate the proposed method using lane-change scenarios with a varying number of vehicles.

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Section: Related Workmentioning

confidence: 99%

Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization

Hart

Rychly

Knoll

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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“…This cooperation would allow the safe interaction with other vehicles, whether with a human driver or driverless. Although some of the proposed solutions already handle the presence of other vehicles in the road [16][17][18], by handling static and dynamic obstacles and adapting the trajectory accordingly, the cooperative driving is not yet achieved. Cooperative driving demands the information exchange and control strategies deployed in all the vehicles involved following a two-way communication.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Hussein

Marín-Plaza

García

et al. 2018

Journal of Advanced Transportation

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Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

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“…Firstly, different convex decomposition methods, such as cell decomposition and reference-frame methods are utilized to enumerate different homotopy classes in the unstructured environments [15]- [19], then suboptimal paths are generated in each homotopy class. The idea of generating distinct topologies first of all, has also been used for onroad situation [20]- [22]. Bender et al [22] distinguish different maneuvers according to surrounding dynamic vehicles.…”

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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The combinatorial aspect of motion planning: Maneuver variants in structured environments

Cited by 67 publications

References 15 publications

Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization

Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

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

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