| 1. |
|
|
| 2. |
|
|
| 3. |
- Abella, J., et al.
(författare)
-
SAFEXPLAIN : Safe and Explainable Critical Embedded Systems Based on AI
- 2023
-
Ingår i: Proceedings -Design, Automation and Test in Europe, DATE. - : Institute of Electrical and Electronics Engineers Inc.. - 9783981926378
-
Konferensbidrag (refereegranskat)abstract
- Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.
|
|
| 4. |
- Abella, Jaume, et al.
(författare)
-
SAFEXPLAIN : Safe and Explainable Critical Embedded Systems Based on AI
- 2023
-
Ingår i: DATE 23: Design, Automation And Test In Europe. - 9783981926378 ; , s. 1-6
-
Konferensbidrag (refereegranskat)abstract
- Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism. © 2023 EDAA.
|
|
| 5. |
- Andersson, Jonas, et al.
(författare)
-
Hello human, can you read my mind?
- 2017
-
Ingår i: ERCIM News. - Sophia Antipolis Cedex : European Research Consortium for Informatics and Mathematics (ERCIM). - 0926-4981 .- 1564-0094. ; :109, s. 36-37
-
Forskningsöversikt (refereegranskat)abstract
- For safety reasons, autonomous vehicles should communicate their intent rather than explicitly invite people to act. At RISE Viktoria in Sweden, we believe this simple design principle will impact how autonomous vehicles are experienced in the future.
|
|
| 6. |
- Andersson, Jonas, et al.
(författare)
-
Study of communication needs ininteraction between trucks and surrounding traffic in platooning
- 2017
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Platooning will soon likely to be common on Swedish roads and the potential for fuel savings in the transport sector is high. This pre-study project explores the need for external signaling in platoons to avoid any cut-ins from surrounding vehicles whose drivers are unaware that their actions may cause a loss of fuel saving. Interviews with truck drivers created an understanding of how they experience the behavior of the surrounding traffic. The scenarios that are highlighted where unaware cut-ins may occur are mainly on-ramps and while overtaking on highway. Car drivers highlighted that overtaking may be a problem, especially on 2+1 roads. Communication needs elicited in workshops with drivers mainly concerned the movement patterns and properties of vehicles, e.g. speed, direction, gaps and length of the platoon. Barriers that were identified for external signaling is that trailers are constantly rotating between different tractors. This may require that more trailers than tractors need to be equipped with communication devices. To evaluate the potential impact of external signaling simulation could be used, where a driving simulator could be used to evaluate the perception of car- and truck drivers. Different means of communication, behavior, driving close together or lighting could be subject to evaluation. The long-term learning effect and behavioral adaptation to platooning in traffic is also important to study. It was found that there are large regional behavioral differences in traffic. Naturalistic data from the US, indicate that there are no cut-ins if the distance between trucks is < 30 m. In Europe, the data collected from ETPC indicate that there is up to one cut-in every 15 km on highways. The data from the ETPC is however very sparse compared to the US study. In Sweden, it does not seem to be a specific need for external signaling since very few cut-ins occur. In Europe, more cut-ins occur and external signaling could help to save fuel. It is however unclear what long-term effects external signaling may have. Further studies are suggested to study if short platooning distance (10-20 meters) is sufficient to deter surrounding traffic from cut-ins.
|
|
| 7. |
- Aramrattana, Maytheewat, 1988-, et al.
(författare)
-
A Novel Risk Indicator for Cut-In Situations
- 2020
-
Ingår i: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. - Piscataway, NJ : Institute of Electrical and Electronics Engineers Inc.. - 9781728141497 - 9781728141503
-
Konferensbidrag (refereegranskat)abstract
- Cut-in situations occurs when a vehicle intentionally changes lane and ends up in front of another vehicle or in-between two vehicles. In such situations, having a method to indicate the collision risk prior to making the cut-in maneuver could potentially reduce the number of sideswipe and rear end collisions caused by the cut-in maneuvers. This paper propose a new risk indicator, namely cut-in risk indicator (CRI), as a way to indicate and potentially foresee collision risks in cut-in situations. As an example use case, we applied CRI on data from a driving simulation experiment involving a manually driven vehicle and an automated platoon in a highway merging situation. We then compared the results with time-to-collision (TTC), and suggest that CRI could correctly indicate collision risks in a more effective way. CRI can be computed on all vehicles involved in the cut-in situations, not only for the vehicle that is cutting in. Making it possible for other vehicles to estimate the collision risk, for example if a cut-in from another vehicle occurs, the surrounding vehicles could be warned and have the possibility to react in order to potentially avoid or mitigate accidents.
|
|
| 8. |
- Aramrattana, Maytheewat, 1988-
(författare)
-
A Simulation-Based Safety Analysis of CACC-Enabled Highway Platooning
- 2018
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cooperative Intelligent Transport Systems (C-ITS) enable actors in the transport systems to interact and collaborate by exchanging information via wireless communication networks. There are several challenges to overcome before they can be implemented and deployed on public roads. Among the most important challenges are testing and evaluation in order to ensure the safety of C-ITS applications.This thesis focuses on testing and evaluation of C-ITS applications with regard to their safety using simulation. The main focus is on one C-ITS application, namely platooning, that is enabled by the Cooperative Adaptive Cruise Control (CACC) function. Therefore, this thesis considers two main topics: i) what should be modelled and simulated for testing and evaluation of C-ITS applications? and ii) how should CACC functions be evaluated in order to ensure safety?When C-ITS applications are deployed, we can expect traffic situations which consist of vehicles with different capabilities, in terms of automation and connectivity. We propose that involving human drivers in testing and evaluation is important in such mixed traffic situations. Considering important aspects of C-ITS including human drivers, we propose a simulation framework, which combines driving-, network-, and traffic simulators. The simulation framework has been validated by demonstrating several use cases in the scope of platooning. In particular, it is used to demonstrate and analyse the safety of platooning applications in cut-in situations, where a vehicle driven by a human driver cuts in between vehicles in platoon. To assess the situations, time-to-collision (TTC) and its extensions are used as safety indicators in the analyses.The simulation framework permits future C-ITS research in other fields such as human factors by involving human drivers in a C-ITS context. Results from the safety analyses show that cut-in situations are not always hazardous, and two factors that are the most highly correlated to the collisions are relative speed and distance between vehicles at the moment of cutting in. Moreover, we suggest that to solely rely on CACC functions is not sufficient to handle cut-in situations. Therefore, guidelines and standards are required to address these situations properly.
|
|
| 9. |
- Aramrattana, Maytheewat, et al.
(författare)
-
A Simulation Study on Effects of Platooning Gaps on Drivers of Conventional Vehicles in Highway Merging Situations
- 2021
-
Ingår i: IEEE transactions on intelligent transportation systems (Print). - Piscataway, NJ : Institute of Electrical and Electronics Engineers Inc.. - 1524-9050 .- 1558-0016.
-
Tidskriftsartikel (refereegranskat)abstract
- Platooning refers to a group of vehicles that--enabled by wireless vehicle-to-vehicle (V2V) communication and vehicle automation--drives with short inter-vehicular distances. Before its deployment on public roads, several challenging traffic situations need to be handled. Among the challenges are cut-in situations, where a conventional vehicle--a vehicle that has no automation or V2V communication--changes lane and ends up between vehicles in a platoon. This paper presents results from a simulation study of a scenario, where a conventional vehicle, approaching from an on-ramp, merges into a platoon of five cars on a highway. We created the scenario with four platooning gaps: 15, 22.5, 30, and 42.5 meters. During the study, the conventional vehicle was driven by 37 test persons, who experienced all the platooning gaps using a driving simulator. The participants' opinions towards safety, comfort, and ease of driving between the platoon in each gap setting were also collected through a questionnaire. The results suggest that a 15-meter gap prevents most participants from cutting in, while causing potentially dangerous maneuvers and collisions when cut-in occurs. A platooning gap of at least 30 meters yield positive opinions from the participants, and facilitating more smooth cut-in maneuvers while less collisions were observed.
|
|
| 10. |
- Aramrattana, Maytheewat, et al.
(författare)
-
Dimensions of cooperative driving, ITS and automation
- 2015
-
Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - Piscataway, NJ : IEEE Press. - 9781467372664 ; , s. 144-149
-
Konferensbidrag (refereegranskat)abstract
- Wireless technology supporting vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communication, allow vehicles and infrastructures to exchange information, and cooperate. Cooperation among the actors in an intelligent transport system (ITS) can introduce several benefits, for instance, increase safety, comfort, efficiency.Automation has also evolved in vehicle control and active safety functions. Combining cooperation and automation would enable more advanced functions such as automated highway merge and negotiating right-of-way in a cooperative intersection. However, the combination have influences on the structure of the overall transport systems as well as on its behaviour. In order to provide a common understanding of such systems, this paper presents an analysis of cooperative ITS (C-ITS) with regard to dimensions of cooperation. It also presents possible influence on driving behaviour and challenges in deployment and automation of C-ITS.
|
|
