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Collision avoidance between vehicles
Platooning is a concept where multiple vehicles follow one another with short intervehicle distances to improve trac eciency and throughput, and reduce fuel consumption due to reduced aerodynamic resistance. Fuel consumption reduced by around 10% [19] because of constant speed. According to experiments, at 10 m spacing and a velocity of 80 km/h, the reduction in fuel consumption was about 21 percent [36]. Other authors also have similar results on the reduction of fuel consumption due to platooning [37, 38]. Closer the vehicles, better are the results of platooning, but higher are the risks of collision. Japans semi-governmental New Energy and Industrial Technology Development Organization, developed a technology for large and small trucks to safely maintain a 4 m distance between vehicles in a single lane while driving 80 kmph [39]. Whereas in Germany, a project at RWTH Aachen University in Germany operated a platoon of four trucks spaced at 10m intervehicle distance [40]. A European project: Safe Road Trains for the Environment (Sartre) based on platooning has explored using cars and lorries simultaneously at 85 kmph with a gap between each vehicle of 6m[41], whereas [19] mentions the velocity range for heavy vehicle platooning is between 37 to 50 kmph with 10 meters distance between them.
Most of the work has been done on collision avoidance in platoons with vehicles moving in a single lane. However, authors in [42] focus on collision avoidance in a scenario where alternate vehicles from the platoon steer towards right or left to stop somewhere along the diagonal with the vehicle in front. There is a big assumption that the roads have atleast three lanes with platoon moving on the lane in the middle, or in case of roads with just one lane, there is free open space on both sides of the roads. In this chapter, we focus on approaches to avoid collisions in platoons based on dierent scenarios. Some of the modern day vehicles already have Adaptive Cruise Control(ACC) systems which are usually radar-based systems that maintain a particular velocity in case there are no vehicles in front, or a safe distance with the vehicle in front, but the safety gap maintained is pretty big, and it serves neither purposes of platooning. The distance between vehicles can either be constant, which is known as constant spacing policy or velocity dependent, which is also known as constant time gap policy. In the rst type of spacing policy, the distance between the vehicle remains xed irrespective of the speed of the vehicle [43, 44]. [44] states the recommended headway in Germany is 1.8 sec for manual driving in a platoon scenario. Moreover, [43] considers not just the vehicle in front but also the vehicle behind for autonomous driving.
String stability is another critical measure of the safety of ACC vehicles. If a disturbance or an error in the platoon of vehicles magnies down the platoon, the system of is said to be string unstable. If the error is absorbed, the system (or the control behavior) is termed string stable. Usually, the use of on board sensors in an ACC system restricts the vehicle only to have information of the preceding vehicle.
Thus, the following vehicle only reacts based on the vehicle in front. This creates a delay in information transfer and thus a delay in reaction, which has a substantial impact on the string stability.
A system of three or more ACC vehicles is very likely to get string unstable according to Lu, Wang et al. [45] and this was proven in [46]. A minimum intervehicle distance of 1:2m can be achieved for two identical vehicles without endangering a collision, assuming that there is no delay present in the feedback system. In case of a delay, intervehicle distance is deduced based upon the vehicles maximum deceleration ability [21].
Collision avoidance in platooning
One of the main purposes of the introduction of autonomous (ACC/CACC) vehicles is to reduce the number of collisions occurring due to human errors and human factors like fatigue, drowsiness, etc. Automating driving procedure using ACC and CACC control algorithms removes the human factor in driving and can thus be considered as algorithms that reduce collisions. String stability is usually veried to ensure ACC and CACC control algorithms are stable. String stability ensures collision avoidance in a platoon under nominal operational conditions, and other methods are required to ensure collision avoidance in a platoon when the system of vehicles is out of the operational domain. [51] introduces an algorithm for safe collision free platoon braking in the event of a total communication failure. They assume it takes some time (perception response time tprt) to detect a communication loss, and thus the response can begin only after tprt s. Dierent situations like hard braking by the leader, hard braking by any vehicle in the middle of the platoon, etc. are simulated with no communication loss or total communication loss. String stability is proved mathematically using transfer functions. When a vehicle in the middle of the platoon starts braking, the original platoon is split into two. The rst one with the original leader, whereas the vehicle in the middle which starts braking becomes the leader of the second platoon. As the authors consider all vehicles to have the same braking capability, the proposed system and mathematical model might not be valid if dierent vehicles have dierent braking capacities.
[52] focusses on longitudinal control for collision avoidance of ACC vehicle platoon in all conditions. Longitudinal collision avoidance is achieved by keeping a minimum safe distance between vehicles based on the deceleration capacity, and the velocity of these vehicles. On the same lines, [53] proposes vehicle following algorithm based on safe following distance dependent on inter vehicular distance and relative velocity. These approaches are conservative and usually require having a large intervehicle distance thus leading to a decrease in road capacity. [54] proposes laws using which two independent platoons can engage in activities like lane changing, merging of platoons, platoon splitting, etc.
Vehicle Following Models
Human drivers usually try to avoid collision with the vehicle in the front. Vehicle at the back is usually not considered. Although dierent humans drive dierently, the driving behavior can be generalized. In literature, dierent researchers have tried to model this driving behavior into driving models. We shall list a few of the most commonly used vehicle following or driving models.
The microscopic model looks at car/vehicle individually; macroscopic model analyses the trac ow as a whole focussing on characteristics like volume, density, and average speed; mesoscopic analyses is in between macroscopic and microscopic ow models. Mesoscopic models focus on characteristics like headways, spacings, speeds and speed dierences. We shall mostly look at the microscopic trac models which include vehicle following models. Most commonly used microscopic models are based on follow-the-leader concept where the subject vehicle ensures it doesn’t crash into the vehicle in front.
Coordinated CAV applications
There are various applications based on V2V communications which will help reduce trac and accidents in the future. Some of these applications which have interested researchers are platooning, coordinated intersection clearance, ramp merging and round about clearance. Platooning has already been detailed before, in this section, we look at the other applications. When multiple vehicles are approaching an intersection, ideally, they should clear the intersection one after another without any vehicle requiring to come to a halt. Moreover, the intersection should be occupied by the next vehicle as soon as the vehicle before quits the intersection. These intersection clearance algorithms can either be centralized or decentralized. Rios-Torres et al. [77] produce a well-written survey of work on centralized and decentralized coordination for collision avoidance and intersection clearance at intersections. Intersection clearance algorithms are evaluated by comparing their performance with the results on trac light based approach.
[78] Gives an overview of the coordination between cooperative autonomous vehicles for collision avoidance in intersections and other cases. Three major issues to solve coordination problems namely, sensing (and signal processing), communication, and control are discussed. Techniques used for vehicle coordination can be broadly split into 1. Rule-based coordination, 2. Optimization based coordination.
Dierent centralized and decentralized control algorithms are discussed. [79,80] introduces the concept of decentralized coordination problem solving and centralized coordination method for intersection management. The coordination strategy decided by a centralized server is optimal as all vehicles implement controls generated by the centralized server. The decentralized system has two steps:
1.: Decision order: It decides the sequence in which the participating vehicles are oered to make their control choice. there are multiple options: FIFO, distance to the intersection, etc. to decide the ordering.
2.: Sequential control computation: Each independent vehicle makes a control decision such that it either enters the intersection after all vehicles (1… i-1) have left or enters the intersection before any of the vehicle (1…i-1) has entered.
Table of contents :
1 Introduction
1.1 General Introduction
1.2 Methodology
1.3 Contribution
1.4 Structure of thesis
2 State of the Art
2.1 Collision avoidance between vehicles
2.1.1 Platooning
2.1.2 Collision avoidance in platooning
2.1.3 Vehicle Following Models
2.1.4 Coordinated CAV applications
2.1.5 Centralized vs decentralized controller
2.2 Types of errors
2.2.1 Communication error
2.2.2 Localization error
2.2.3 Control error
2.2.4 Model mismatch
2.2.5 Perception response time
2.2.6 Achievable braking capacity
2.3 Problem formulation
3 Analysis of Centralized Controller Operation
3.1 Centralized Controller Operation
3.1.1 Obstacle detection and gathering vehicle information
3.1.2 Model predictive control basic principle
3.1.3 Mathematical formulation of the MPC Controller
3.2 Modeling Errors inuencing the centralized controller
3.2.1 Model Mismatch
3.2.2 Control Error
3.2.3 Localization Error
3.2.4 Communication Error
4 Robust Centralized Controller
4.1 Centralized controller conguration
4.1.1 Simulation parameters
4.2 Buer
4.3 Robustness to Model Mismatch
4.3.1 Interface with Driving simulator
4.4 Impact of Control errors
4.5 Robustness to Communication error
4.5.1 Evaluation set 1
4.5.2 Evaluation set 2
4.6 Robustness to Localization error
4.6.1 Controller model robust to localization error
4.6.2 Evaluation of the robust controller
4.6.3 Warning for enhanced safety
4.7 Robustness of the controller to various errors
4.7.1 Communication Overhead
4.8 Parameter Sensitivity
5 Conclusions and Perspectives
5.1 Conclusions
5.2 Perspectives
A List of Publications and Contributions
