Braking Strategy for an Autonomous Vehicle in a Mixed Traffic Scenario

Patel, Raj Haresh; Härri, Jérôme; Bonnet, Christian

doi:10.5220/0006307702680275

Cited by 9 publications

(3 citation statements)

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“…The Doppler effect is then neglected. The distance corresponds to a reasonable braking distance for an autonomous vehicle at a speed of 50 km/h [21][22][23][24]. The third interfering vehicle is added (blue vehicle), to evaluate the impact of its signal emitted on the communication between the intended transmitter and the receiver depending on the position of the interfering vehicle.…”

Section: Pdr Performancementioning

confidence: 99%

Optical CDMA MAC Evaluation in Vehicle-to-Vehicle Visible Light Communications

et al. 2022

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One of the most critical challenges in vehicle-to-vehicle (V2V) visible light communications (VLCs) is the access capability of multi-users over the same channel. VLC links are known to be line of sight (LOS) and highly directional compared to the commonly used radio-frequency (RF) technologies, but multi-user interference can still be problematic. Consequently, it is necessary to develop a medium access control (MAC) protocol for VLC. In the present work, we propose a novel optical code division multiple access (OCDMA) protocol. The fundamentals of the VLC channel are presented, and the interference problem is discussed in more detail. We also explain the CDMA MAC protocol and its adaptation from optical fiber communications. The simulations with our OCDMA protocol based on pseudo-noise (PN) sequence codes are presented, and the results are compared with those obtained using a solution based on VLC and optical orthogonal codes (OOCs) from the OCDMA gold standard. Finally, the results show that PN sequence OCDMA can provide sufficiently high performance and is particularly attractive because there are no synchronization requirements between nodes in real V2V scenarios.

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Section: Pdr Performancementioning

confidence: 99%

Optical CDMA MAC Evaluation in Vehicle-to-Vehicle Visible Light Communications

et al. 2022

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“…If all the vehicles of a network are autonomous and connected to each other, a coordinated braking strategy can be devised to avoid both rear and front-end collisions improving traffic safety standards enormously (Patel et al, 2017). But this ideal scenario may not be easily implementable at least at the initial stages of a CAVs launch; replacing all the non-fully automated vehicles at once is unrealistic.…”

Section: Myth Nine: Mixed Traffic Situations Present No Threatmentioning

confidence: 99%

Examining the myths of connected and autonomous vehicles: analysing the pathway to a driverless mobility paradigm

Dani

Nikitas

Njoya

2019

IJATM

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Connected and autonomous vehicles (CAVs) could become the most powerful mobility intervention in the history of human race; possibly greater than the conception of the wheel itself or the shift from horse-carriages to automobiles. Despite CAVs' likely traffic safety, economic, environmental, social inclusion and network performance benefits their full-scale implementation may not be as predictable, uncomplicated, acceptable and risk-free as it is often communicated by a large share of automotive industries, policy-makers and transport experts. Framing an 'unproven', 'disruptive' and 'life-changing' intervention, primarily based on its competitive advantages over today's conventional automobile technologies, may create misconceptions, overreaching expectations and room for errors that societies need to be cautious about. This article 'tests' eleven myths referring to an overly optimistic CAVs' development and adoption timeline. This approach highlights unresolved issues that need to be addressed before an inescapable CAV-based mobility paradigm transition takes place and provides relevant policy recommendations of how to achieve that.

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“…Various studies have been carried out for this purpose. Patel et al [2] proposed a scenario for embodying different brake strategies of AVs in a mixed traffic flow. Virdi et al [3] calculated the potential for collision in different scenarios according to the mixed rate of AVs by intersection, and proved that potential for collisions decreased as the ratio of AVs increased.…”

Section: Introductionmentioning

confidence: 99%

Development of Risk-Situation Scenario for Autonomous Vehicles on Expressway Using Topic Modeling

Chae

Kim

Jang

et al. 2022

Journal of Advanced Transportation

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Growing interest has recently been paid to the development of autonomous vehicle scenarios, and corresponding research is being conducted on various methodologies and on the generation of scenarios including technological elements. However, most studies have focused on frequently-occurring accident types or representative accident situations; thus, there is a lack of studies on scenarios considering unpredictable accidents. Proper preparation is required for accident situations because even a small traffic accident that is less likely to occur can lead to fatalities if it is difficult to predict. Accordingly, this study established accident situations based on the Pegasus layer model by using unstructured text data to explain traffic accidents on expressways in Korea. The established accident situations were classified into three types (Typical Traffic/Critical Traffic/Edge Case) according to frequency. Topic modeling was applied to the Edge Case class, i.e., the least likely to occur and thus difficult to predict, to analyze the characteristics of groups and develop risk-situation scenarios for autonomous vehicles based on actual accident data.

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Braking Strategy for an Autonomous Vehicle in a Mixed Traffic Scenario

Cited by 9 publications

References 0 publications

Optical CDMA MAC Evaluation in Vehicle-to-Vehicle Visible Light Communications

Optical CDMA MAC Evaluation in Vehicle-to-Vehicle Visible Light Communications

Examining the myths of connected and autonomous vehicles: analysing the pathway to a driverless mobility paradigm

Development of Risk-Situation Scenario for Autonomous Vehicles on Expressway Using Topic Modeling

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