| 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. |
- Esposito, Francesco, et al.
(författare)
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Learning Task Constraints in Visual-Action Planning from Demonstrations
- 2021
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Ingår i: 30th IEEE International Conference on Robot & Human Interactive Communication, RO-MAN 2021. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 131-138
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Konferensbidrag (refereegranskat)abstract
- Visual planning approaches have shown great success for decision making tasks with no explicit model of the state space. Learning a suitable representation and constructing a latent space where planning can be performed allows non-experts to setup and plan motions by just providing images. However, learned latent spaces are usually not semantically-interpretable, and thus it is difficult to integrate task constraints. We propose a novel framework to determine whether plans satisfy constraints given demonstrations of policies that satisfy or violate the constraints. The demonstrations are realizations of Linear Temporal Logic formulas which are employed to train Long Short-Term Memory (LSTM) networks directly in the latent space representation. We demonstrate that our architecture enables designers to easily specify, compose and integrate task constraints and achieves high performance in terms of accuracy. Furthermore, this visual planning framework enables human interaction, coping the environment changes that a human worker may involve. We show the flexibility of the method on a box pushing task in a simulated warehouse setting with different task constraints.
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| 3. |
- Gaspar Sánchez, José Manuel, et al.
(författare)
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Foresee the Unseen : Sequential Reasoning about Hidden Obstacles for Safe Driving
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Safe driving requires autonomous vehicles to anticipate potential hidden traffic participants and other unseen objects, such as a cyclist hidden behind a large vehicle, or an object on the road hidden behind a building. Existing methods are usually unable to consider all possible shapes and orientations of such obstacles. They also typically do not reason about observations of hidden obstacles over time, leading to conservative anticipations. We overcome these limitations by (1) modeling possible hidden obstacles as a set of states of a point mass model and (2) sequential reasoning based on reachability analysis and previous observations. Based on (1), our method is safer, since we anticipate obstacles of arbitrary unknown shapes and orientations. In addition, (2) increases the available drivable space when planning trajectories for autonomous vehicles. In our experiments, we demonstrate that our method, at no expense of safety, gives rise to significant reductions in time to traverse various intersection scenarios from the CommonRoad Benchmark Suite.
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| 4. |
- Gaspar Sánchez, José Manuel, et al.
(författare)
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Foresee the Unseen : Sequential Reasoning about Hidden Obstacles for Safe Driving
- 2022
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Ingår i: 2022 IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 255-264
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Konferensbidrag (refereegranskat)abstract
- Safe driving requires autonomous vehicles to anticipate potential hidden traffic participants and other unseen objects, such as a cyclist hidden behind a large vehicle, or an object on the road hidden behind a building. Existing methods are usually unable to consider all possible shapes and orientations of such obstacles. They also typically do not reason about observations of hidden obstacles over time, leading to conservative anticipations. We overcome these limitations by (1) modeling possible hidden obstacles as a set of states of a point mass model and (2) sequential reasoning based on reachability analysis and previous observations. Based on (1), our method is safer, since we anticipate obstacles of arbitrary unknown shapes and orientations. In addition, (2) increases the available drivable space when planning trajectories for autonomous vehicles. In our experiments, we demonstrate that our method, at no expense of safety, gives rise to significant reductions in time to traverse various intersection scenarios from the CommonRoad Benchmark Suite.
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| 5. |
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| 6. |
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| 7. |
- Karlsson, Jesper, et al.
(författare)
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Calibrating Driving Styles in Motion Planning for Autonomous Vehicles
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- To display perceivably different driving styles is an important ability for autonomous vehicles in real-life traffic scenarios. By encouraging predictable driving styles, we can promote trust and collaboration between vehicle and human drivers in traffic. However, many motion planners lack the ability to provide different driving styles using one method.As a result, many applications are overly defensive to ensure safety, and can not be tailored to the users' preferences. In this work, we build on our previous works on encoding perceivable driving styles using Signal Temporal Logic (STL) and generating motion planning trajectories using user preferences. We refine our previously proposed spatial constraints based on the Responsibility-Sensitive Safety (RSS) model. We illustrate how the motion planner can be parameterized to produce aggressive, neutral and defensive driving behaviors, respectively. We evaluate the resulting driving styles on a set of real-life inspired driving scenarios, modeled in the Carla simulator and provide a detailed statistical analysis of the generated trajectories.
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| 8. |
- Karlsson, Jesper, et al.
(författare)
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Encoding Human Driving Styles in Motion Planning for Autonomous Vehicles
- 2021
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Ingår i: 2021 IEEE International Conference on Robotics and Automation (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 11262-11268
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Konferensbidrag (refereegranskat)abstract
- Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style.
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| 9. |
- Koschi, Markus, et al.
(författare)
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Computationally Efficient Safety Falsification of Adaptive Cruise Control Systems
- 2019
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Konferensbidrag (refereegranskat)abstract
- Falsification aims to disprove the safety of systems by providing counter-examples that lead to a violation of safety properties. In this work, we present two novel falsification methods to reveal safety flaws in adaptive cruise control (ACC) systems of automated vehicles. Our methods use rapidly- exploring random trees to generate motions for a leading vehicle such that the ACC under test causes a rear-end collision. By considering unsafe states and searching backward in time, we are able to drastically improve computation times and falsify even sophisticated ACC systems. The obtained collision scenarios reveal safety flaws of the ACC under test and can be directly used to improve the system’s design. We demonstrate the benefits of our methods by successfully falsifying the safety of state-of-the-art ACC systems and comparing the results to that of existing approaches.
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| 10. |
- Koschi, Markus, et al.
(författare)
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Set-Based Prediction of Pedestrians in Urban Environments Considering Formalized Traffic Rules
- 2018
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Konferensbidrag (refereegranskat)abstract
- Set-based predictions can ensure the safety of planned motions, since they provide a bounded region which includes all possible future states of nondeterministic models of other traffic participants. However, while autonomous vehicles are tested in urban environments, a set-based prediction tailored to pedestrians does not exist yet. This paper addresses this problem and presents an approach for set-based predictions of pedestrians using reachability analysis. We obtain tight over-approximations of pedestrians’ reachable occupancy by incorporating the dynamics of pedestrians, contextual information, and traffic rules. In addition, since pedestrians often disregard traffic rules, our constraints automatically adapt so that such behaviors are included in the prediction. Using datasets of recorded pedestrians, we validate our proposed method and demonstrate its use for evasive maneuver planning of automated vehicles.
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