Cooperative driving of connected autonomous vehicles using responsibility-sensitive safety (RSS) rules

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights’ phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles’ speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed.

Section: Autonomous Intersection Management: Reviewmentioning

confidence: 99%

Section: Autonomous Intersection Management: Reviewmentioning

confidence: 99%

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Mualla

Gaud

Calvaresi

et al. 2023

“…Zhao, C. et al confirmed that communication based between vehicles to improve the lane change performance of RSS is efficient and reasonable by increasing the utilization of limited road resources [14]. Khayatian, M. et al introduced a new definition of RSS rules applicable to all scenarios and proposed a CAV (connected autonomous vehicle) driving algorithm [15]. However, Zhao, C. et al [14] apply vehicle-to-vehicle communication, and Khayatian, M. et al [15] includes the premise that vehicle-to-vehicle (V2V) communication should be possible to perform with CAVs.…”

Section: Related Workmentioning

confidence: 99%

“…Khayatian, M. et al introduced a new definition of RSS rules applicable to all scenarios and proposed a CAV (connected autonomous vehicle) driving algorithm [15]. However, Zhao, C. et al [14] apply vehicle-to-vehicle communication, and Khayatian, M. et al [15] includes the premise that vehicle-to-vehicle (V2V) communication should be possible to perform with CAVs. Therefore, there is a limit to the application of the V2V communication technology in a non-preceded state.…”

Section: Related Workmentioning

confidence: 99%

On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Kim

Yu²,

Kim³

et al. 2021

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

“…Zhao, C. et al (2021) proposed an allocation of priority based on inter-vehicle communication as a way to improve lane change performance by utilizing RSS models and confirmed that limited road resources can be utilized through inter-vehicle phenomena [ 12 ]. Khayatian, M. (2021) proposed a generalized version of RSS rules applicable to all driving scenarios via vehicle-to-vehicle connections and presented a cooperative driving algorithm for CAV based on the proposed RSS model [ 13 ]. Orzechowski, P. F. et al (2019) suggested a safety verification technique based on the RSS model for merging and intersection scenarios and confirmed that the proposed model guarantees safety and efficiency [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

RSS Model Improvement Considering Road Conditions for the Application of a Variable Focus Function Camera

Kim

2023

The automobile industry has developed dramatically in recent years, the supply of vehicles has also increased, and thus it has become deeply established in everyday life. Recently, as the supply of vehicles with autonomous driving functions increases, the safety of vehicles is also an emerging issue. Various car-following models for the safe driving of vehicles have long been studied by various people, and recently, a Responsibility-Sensitive Safety (RSS) model has been proposed by Mobileye. However, in existing car-following models or the RSS model, the safe distance between vehicles is presented using only vehicle speed and acceleration information, so there is a limitation in that it cannot respond to changes in road conditions due to the weather. In this paper, in order to ensure safety when the RSS model is applied to a variable focus function camera, an improved RSS model is presented in consideration of the changes in road conditions due to changes in weather, and a safety distance is derived based on the proposed model.