Journal
IEEE ACCESS
Volume 7, Issue -, Pages 180039-180053Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2959432
Keywords
Autonomous driving; trajectory planning; obstacle avoidance; collision checking
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Funding
- Spanish Ministry of Science, Innovation and Universities [DPI2017-86915-C3-1-R]
- European Commission through the Project PRYSTINE [ECSEL-783190-2]
- European Commission through the Project SECREDAS [ECSEL-783119-2]
- Community of Madrid through SEGVAUTO 4.0-CM Programme [S2018-EMT-4362]
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Autonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide range of driving scenarios. Typical approaches for motion planning focus on trajectory optimization by applying computation-intensive algorithms, rather than finding a balance between optimatily and computing time. However, for on-road automated driving at medium and high speeds, determinism is necessary at high sampling rates. This work presents a trajectory planning algorithm that is able to provide safe, human-like and comfortable trajectories by using cost-effective primitives evaluation based on quintic Bezier curves. The proposed method is able to consider the kinodynamic constrains of the vehicle while reactively handling dynamic real environments in real-time. The proposed motion planning strategy has been implemented in a real experimental platform and validated in different real operating environments, successfully overcoming typical urban traffic scenes where both static and dynamic objects are involved.
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