Abstract
This thesis presents behavior and motion planning method that enables Autonomous Vehicles (AV) to achieve SuperHuman driving performance in terms of safety, efficiency and comfort. The developed method enables synergy of research in behavior and motion planning for Automated Driving, with research in eco-driving community, which target mainly complementary problem variations. Established approach in eco-driving is considering long planning horizons and multiple constraints (i.e. traffic lights, speed limits, etc.), but exclusively single lane driving. On the other hand, motion planning for Automated Driving considers multilane driving, but short planning horizons and decoupled (or hierarchical) solutions, focused on effectively reacting to the changing situations, and not on the long-term optimal behavior.
As a result of the synergy, developed search-based optimal motion planning (SBOMP) solution enables optimal Automated Driving scalable to various challenging scenarios in urban, rural and highway environment. As a highlight, SBOMP enables, what is believed to be, the first demonstration of optimal multilane diving in dense traffic with traffic lights, while achieving SuperHuman driving performance. Even though, this scenario is pretty common in everyday driving, it was not tackled by any of these research communities before.
The presented SBOMP framework is also extended to the third use-case, Performance Driving. By considering a more detailed vehicle model, SBOMP enables minimum lap-time driving on a slippery road, effectively entering and exiting drifting maneuvers and switching between right and left turns.
The presented work is extensively tested in simulation, benchmarked with human driving behavior acquired in driving simulator study and in-vehicle testing on proving ground. The results show that in challenging urban driving scenario with traffic lights, AV outperforms even the best human drivers in terms of safety, efficiency and comfort. While human drivers violate traffic rules and even cause crashes, by using predictive planning, AV manages to drive smoothly through the traffic.
Hopefully, this work contributes to the effort that Autonomous Vehicles become the first mass product of intelligent mobile robots in our society.
As a result of the synergy, developed search-based optimal motion planning (SBOMP) solution enables optimal Automated Driving scalable to various challenging scenarios in urban, rural and highway environment. As a highlight, SBOMP enables, what is believed to be, the first demonstration of optimal multilane diving in dense traffic with traffic lights, while achieving SuperHuman driving performance. Even though, this scenario is pretty common in everyday driving, it was not tackled by any of these research communities before.
The presented SBOMP framework is also extended to the third use-case, Performance Driving. By considering a more detailed vehicle model, SBOMP enables minimum lap-time driving on a slippery road, effectively entering and exiting drifting maneuvers and switching between right and left turns.
The presented work is extensively tested in simulation, benchmarked with human driving behavior acquired in driving simulator study and in-vehicle testing on proving ground. The results show that in challenging urban driving scenario with traffic lights, AV outperforms even the best human drivers in terms of safety, efficiency and comfort. While human drivers violate traffic rules and even cause crashes, by using predictive planning, AV manages to drive smoothly through the traffic.
Hopefully, this work contributes to the effort that Autonomous Vehicles become the first mass product of intelligent mobile robots in our society.
| Original language | English |
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| Qualification | Doctor of Technology |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 6 Dec 2019 |
| Publication status | Published - 6 Dec 2019 |
Keywords
- Autonomous Vehicles
- Motion Planning
- eco-driving
- Automated Driving
- Performance Driving
- SuperHuman Autonomous Vehicles