| 1. |
- Althoff, Matthias, et al.
(författare)
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Provably-Correct and Comfortable Adaptive Cruise Control
- 2020
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- Adaptive cruise control is one of the most common comfort features of road vehicles. Despite its large market penetration, current systems are not safe in all driving conditions and require supervision by human drivers. While several previous works have proposed solutions for safe adaptive cruise control, none of these works considers comfort, especially in the event of cut-ins. We provide a novel solution that simultaneously meets our specifications and provides comfort in all driving conditions including cut-ins. This is achieved by an exchangeable nominal controller ensuring comfort combined with a provably correct fail-safe controller that gradually engages an emergency maneuver—this ensures comfort, since most threats are already cleared before emergency braking is fully activated. As a conse- quence, one can easily exchange the nominal controller without having to re-certify the overall system safety. We also provide the first user study for a provably correct adaptive cruise controller. It shows that even though our approach never causes an accident, passengers rate the performance as good as a state-of-the-art solution that does not ensure safety.
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| 2. |
- Liu, Edmond Irani, et al.
(författare)
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Provably-Safe Cooperative Driving via Invariably Safe Sets
- 2020
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Konferensbidrag (refereegranskat)abstract
- We address the problem of provably-safe cooperative driving for a group of vehicles that operate in mixed traffic scenarios, where both autonomous and human-driven vehicles are present. Our method is based on Invariably Safe Sets (ISSs), which are sets of states that let each of the cooperative vehicles remain safe for an infinite time horizon. The potential conflicts between the ISSs of a group of cooperative vehicles are resolved by examining and negotiating their Safe Maneuver Corridors. As a result, each vehicle obtains its negotiated ISS, which is used as target sets for motion planning. We demonstrate the applicability and benefits of our method on various traffic scenarios from the CommonRoad benchmark suite.
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| 3. |
- Mitsioni, Ioanna, et al.
(författare)
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Safe Data-Driven Contact-Rich Manipulation
- 2020
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Konferensbidrag (refereegranskat)abstract
- In this paper, we address the safety of data-driven control for contact-rich manipulation. We propose to restrict the controller’s action space to keep the system in a set of safe states. In the absence of an analytical model, we show how Gaussian Processes (GP) can be used to approximate safe sets. We disable inputs for which the predicted states are likely to be unsafe using the GP. Furthermore, we show how locally designed feedback controllers can be used to improve the execution precision in the presence of modelling errors. We demonstrate the benefits of our method on a pushing task with a variety of dynamics, by using known and unknown surfaces and different object loads. Our results illustrate that the proposed approach significantly improves the performance and safety of the baseline controller.
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| 4. |
- Pek, Christian, et al.
(författare)
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CommonRoad Drivability Checker : Simplifying the Development and Validation of Motion Planning Algorithms
- 2020
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Konferensbidrag (refereegranskat)abstract
- Collision avoidance, kinematic feasibility, and road-compliance must be validated to ensure the drivability of planned motions for autonomous vehicles. Although these tasks are highly repetitive, computationally efficient toolboxes are still unavailable. The CommonRoad Drivability Checker— an open-source toolbox—unifies these mentioned checks. It is compatible with the CommonRoad benchmark suite, which additionally facilitates the development of motion planners. Our toolbox drastically reduces the effort of developing and validating motion planning algorithms. Numerical experiments show that our toolbox is real-time capable and can be used in real test vehicles.
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| 5. |
- Pek, Christian
(författare)
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Provably Safe Motion Planning for Autonomous Vehicles Through Online Verification
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis introduces fail-safe motion planning as the first approach to guarantee legal safety of autonomous vehicles in arbitrary traffic situations. The proposed safety layer verifies whether intended trajectories comply with legal safety and provides fail-safe trajectories when intended trajectories result in safety-critical situations. The presented results indicate that the use of fail-safe motion planning can drastically reduce the number of traffic accidents.
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| 6. |
- Pek, Christian, et al.
(författare)
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Using online verification to prevent autonomous vehicles from causing accidents
- 2020
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Ingår i: Nature Machine Intelligence. - : Nature Research. - 2522-5839. ; 2:9, s. 518-528
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Tidskriftsartikel (refereegranskat)abstract
- Ensuring that autonomous vehicles do not cause accidents remains a challenge. We present a formal verification technique for guaranteeing legal safety in arbitrary urban traffic situations. Legal safety means that autonomous vehicles never cause accidents although other traffic participants are allowed to perform any behaviour in accordance with traffic rules. Our technique serves as a safety layer for existing motion planning frameworks that provide intended trajectories for autonomous vehicles. We verify whether intended trajectories comply with legal safety and provide fallback solutions in safety-critical situations. The benefits of our verification technique are demonstrated in critical urban scenarios, which have been recorded in real traffic. The autonomous vehicle executed only safe trajectories, even when using an intended trajectory planner that was not aware of other traffic participants. Our results indicate that our online verification technique can drastically reduce the number of traffic accidents.
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