Authors:
Christian Wriedt
1
and
Christoph Beierle
2
Affiliations:
1
Audi Electronics Venture GmbH, Sachsstr. 20, 85080 Gaimersheim and Germany
;
2
Department of Computer Science, FernUniversität in Hagen, 58084 Hagen and Germany
Keyword(s):
Constraint Logic Programming, Trajectory Planning, Automated Driving Systems, ADAS, Autonomous Driving, Explainable AI.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Simulation and Modeling
;
State Space Search
;
Symbolic Systems
Abstract:
Automated driving systems are a maturing technology that is considered to have a significant impact on mobility. Trajectory Planning is a safety-critical task that plays an important role in automated driving systems. In this paper, we present the implementation of a trajectory planning module called CLPTP (CLPTRAJECTORYPLANNER) using constraint logic programming (CLP) and evaluate it in simulated traffic situations. CLP allows us to express the constraints of the problem of trajectory planning in a declarative way. The approach makes the code less complex and more readable for domain experts compared to code using an imperative programming language. Compared to approaches making use of neural networks to manage the complexity of the problem of trajectory planning, the results of CLPTP are more comprehensible and easier to verify. Thus CLPTP can be seen as a step towards solving the problem of trajectory planning with explainable artificial intelligence. An evaluation of the executio
n time performance of our implementation shows that further research is needed to apply the approach in real world vehicles.
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