Computing the Drivable Area of Autonomous Road Vehicles in Dynamic Road Scenes

Sontges, Sebastian; Althoff, Matthias

doi:10.1109/tits.2017.2742141

Cited by 75 publications

(83 citation statements)

References 34 publications

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“…Due to the lack of comparable algorithms, no comparison to existing work is possible. For the computation of the drivable area, we use the method presented in [23]. Used parameters are listed in Table I.…”

Section: Resultsmentioning

confidence: 99%

“…To consider not only the optimal solution of the motion planning problem, but the space of all solutions, we require the set of reachable states [22]. In particular, we use a socalled anticipated reachable set, which excludes states that will inevitably result in an accident [23] in the time interval t ∈ [t 0 , t f ]. We denote a feasible trajectory as χ(t; x 0 , u(·)), which meets all constraints in (1).…”

Section: Drivable Areamentioning

confidence: 99%

See 1 more Smart Citation

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

Klischat

Althoff

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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124

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Virtual testing of automated vehicles using simulations is essential during their development. When it comes to the testing of motion planning algorithms, one is mainly interested in challenging, critical scenarios for which it is hard to find a feasible solution. However, these situations are rare under usual traffic conditions, demanding an automatic generation of critical test scenarios. We present an approach that automatically generates critical scenarios based on a minimization of the solution space of the vehicle under test. By formulating a scenario parametrization and automatic determination of relevant parameter intervals, we are able to optimize the criticality of complex scenarios. We use evolutionary algorithms to tackle the resulting highly nonlinear optimization problem. Compared to our previous approach, we are now able to handle complex situations, in particular those involving intersections. Finally, we demonstrate our approach by generating critical scenarios from initially uncritical scenarios.All authors are with the Technische Universität München,

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Section: Resultsmentioning

confidence: 99%

Section: Drivable Areamentioning

confidence: 99%

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

Klischat

Althoff

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

124

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“…The authors of [6] determine all reachable positions while ignoring the velocity domain, which results in overly large reachable regions. In our previous work [7], we compute an over-approximation of the reachable set considering position, velocity, and acceleration constraints. This over-approximation can be used to determine the nonexistence of evasive trajectories [8].…”

Section: B Related Workmentioning

confidence: 99%

Worst-case Analysis of the Time-To-React Using Reachable Sets

Sontges

Koschi

Althoff

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Collision mitigation and collision avoidance systems in intelligent vehicles reduce the severity and number of accidents. To determine the optimal point in time at which such systems should intervene, time-based criticality metrics such as the Time-To-React (TTR) are commonly used. The TTR describes the last point in time along the current trajectory at which an evasive trajectory exists. In this paper, we present a novel approach to determine the point in time after which it is guaranteed that no evasive maneuver exists, i.e., by using reachable sets, we over-approximate the TTR. Our deterministic upper bound of the TTR can be used to trigger a collision mitigation system or to find a feasible emergency maneuver which avoids the collision. We demonstrate the efficient computation of the tight over-approximated TTR in different urban and rural traffic scenarios, and compare our results to an estimated TTR using an optimization-based trajectory planner.

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“…Currently, two main topology-based methods have been extensively investigated, namely, the sampling based methods and the combinatorial methods. Sampling based methods group paths laterally shifted from a lane-center with motion primitives smoothly connected [12]- [14]. The maneuvers are generated by clustering paths into the same groups or homotopy classes.…”

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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Computing the Drivable Area of Autonomous Road Vehicles in Dynamic Road Scenes

Cited by 75 publications

References 34 publications

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

Generating Critical Test Scenarios for Automated Vehicles with Evolutionary Algorithms

Worst-case Analysis of the Time-To-React Using Reachable Sets

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

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