| 1. |
- Althoff, Matthias, et al.
(author)
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Provably-Correct and Comfortable Adaptive Cruise Control
- 2020
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In: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858. ; , s. 1-1
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Journal article (peer-reviewed)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. |
- Hoel, Carl-Johan, 1986, et al.
(author)
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Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
- 2020
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In: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 5:2, s. 294-305
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Journal article (peer-reviewed)abstract
- Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This article introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.
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