Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles

Abstract

Vehicle tailgating or simply tailgating is a hazardous driving habit. Tailgating occurs when a vehicle moves very close behind another one while not leaving adequate separation distance in case the vehicle in front stops unexpectedly; this separation distance is technically called “Assured Clear Distance Ahead” (ACDA) or Safe Driving Distance. Advancements in Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have made it of tremendous significance to have an intelligent approach for connected vehicles to avoid tailgating; this paper proposes a new Internet of Vehicles (IoV) based technique that enables connected vehicles to determine ACDA or Safe Driving Distance and Safe Driving Speed to avoid a forward collision. The technique assumes two cases: In the first case, the vehicle has Autonomous Emergency Braking (AEB) system, while in the second case, the vehicle has no AEB. Safe Driving Distance and Safe Driving Speed are calculated under several variables. Experimental results show that Safe Driving Distance and Safe Driving Speed depend on several parameters such as weight of the vehicle, tires status, length of the vehicle, speed of the vehicle, type of road (snowy asphalt, wet asphalt, or dry asphalt or icy road) and the weather condition (clear or foggy). The study found that the technique is effective in calculating Safe Driving Distance, thereby resulting in forward collision avoidance by connected vehicles and maximizing road utilization by dynamically enforcing the minimum required safe separating gap as a function of the current values of the affecting parameters, including the speed of the surrounding vehicles, the road condition, and the weather condition.

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

“…Furthermore, safety is the core objective of autonomous driving. As pointed out by Elsagheer Mohamed et al [24], maintaining an appropriate safety distance from other vehicles and obstacles is vital to avoiding collisions. Therefore, the reward function is computed based on the distance between the vehicle and the obstacle ahead.…”

Data and Energy Impacts of Intelligent Transportation—A Review

“…AVs, including CAVs, are equipped with various sensors like LiDAR, radar, cameras, and ultrasonic sensors. These sensors gather real-time data concerning both the vehicle's functionality and its environment [18]. They are crucial for providing perception, which is key to acquiring accurate and current environmental information.…”

“…Figure 3 shows the typical location of the various sensors [8]. The ADAS system, including the global navigation satellite system (GNSS), is shown in Figure 4, which is an illustration of the functions of each sensor and their location in autonomous vehicles [18]. Based on the information in Figure 4, Table 2 is generated, which summarizes different sensor applications around the vehicle.…”

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“…Modern vehicular communications are organised as a wireless network that enables communication among vehicles (V2V) and infrastructure (V2I). An active Vehicular Ad-hoc Network (VANET) is one that can dynamically adjust its topology and routing based on the road traffic conditions, network scalability, vehicle mobility and network quality [1], [2], yet they are expected to have maximum efficiency in data transmission. Due to the critical nature of their communications, where any disruption could mean the difference between life and death, these networks have to be designed with fault-tolerance in mind.…”