Trans Hudson Exclusive Bus Lane (XBL) Automation Implementation

By Robert James – HNTB, Rizwan Baig – PANYNJ, Jennifer Bates – PANYNJ, & Osman Altan - PANYNJ

BACKGROUNDThe dedicated bus lanes going into Port Authority Bus Terminal (PABT) through the Lincoln Tunnel are high passenger volume lanes that bring commuters into New York City from New Jersey and Pennsylvania. The lane is operational from 6am to 10am each morning in the inbound direction only. The XBL begins at the entrances from I495 and Rt 3 on the west and go through a 360-degree helix approaching the toll plaza and enters the Lincoln tunnel. It ends just past the tunnel on the east as it enters the bus terminal on 4 level ramps.

The terminal is the largest in the United States and the busiest in the world by volume of traffic, serving about 8,000 buses and 225,000 people on an average weekday and more than 65 million people a year. There are more than 2.2 million bus departures from the terminal per year. Most of these busses come through the XBL.

This volume is frequently challenging the current capacity of the XBL during peak travel times. In addition to the loss of life, any incident or deviation in traffic flow can severely affect the traffic flow for busses as well as the commuter lanes through the tunnels. Recent incidents with buses in the tunnel have caused traffic backups for hours having devastating effects on the region costing millions of dollars in lost productivity.

Port Authority is examining the state of the art in collision warning and automated technology that could be deployed on the buses to improve the safety and increase the capacity of the bus lanes. Since the lanes are used by a limited number of buses a deployment could be done for a manageable cost that could potentially have a significant impact to safety and throughput.

PROJECT SCOPEThis project will demonstrate the effects of automation being applied to the XBL to determine what impacts on safety and capacity can be achieved with application of automation on some or all the vehicles. The technology community has come to recognize that connected vehicles instead of fully autonomous self-contained vehicles are the best path to zero fatalities.

The project will begin by equipping 10 buses with a full suite of automated technologies. These will be used to validate the control algorithms and define the equipment to be installed in the ultimate deployment. The project intends to equip all the approximately 3600 vehicles using the XBL with Connected Vehicle Communications connected to the CAN bus onboard with Driver Interface Display. This will be integrated with the Mobileye vision system to provide a basic set of safety and operational applications. This will provide several of the Applications shown on Table 1 below. The project plans on taking this to the next step by automating the vehicle functions for MCI buses already equipped with braking and throttle actuators (approximately 1900) to further improve safety and increase capacity. It is not anticipated that the project will add steering actuators for full vehicle control at this point, but may at a future time.

Technology Application Benefits Vendor(s)Lane Departure Warning Safety MobileyeForward Collision WarningHeadway Monitoring WarningPedestrian Collision Warning

Speed Limit Indication

Intelligent High Beam control

Adaptive Cruise Control Safety/Capacity Wabco

Lane Departure Warning

Automatic Emergency BrakingVehicle-to-Vehicle (V2V): Cooperative Adaptive Cruise Control, Electronic Emergency Brake Light (EEBL), Forward Collision Warning (FCW), Blind Spot Warning (BSW),Intersection Movement Assist (IMA), Lane Change Advisory (LCA), Left turn assist(LTA), Do Not Pass Warning (DNPW).

Safety Connected Vehicle Vendors

Vehicle-to-Infrastructure (V2I): Mechanical health monitoring, Terminal GateAssignment Notification, Terminal Fleet Management, Curve Speed Warning(CSW), Work Zone Warning (WZW), Spot Weather Impact Alerts, and Bus SignalPriority/in-vehicle SPaT (Signal Phase and Timing) visualization.

Mobility/ Operations

Vehicle-to-Pedestrian or Vehicle-to-Phone Communication (V2P): Enables cars toidentify pedestrians crossing streets in unpredictable situations and alerts the driversthrough an application displayed either on a smartphone or on In-Vehicle-Infotainment systems

Safety

Can equip 1600 MCI buses easily

Deployed in 15M Autos and 1500 Buses

(Included in Wabco product below.)

Savari, Delphi, Wave Mobile, Denso,

Veniam, Arabac, Lear

Table 1. XBL Technology Implementations for the Automation Project

While the hardware for actuators, sensors, and communications devices are readily available and have been deployed widely, combining all these together on buses will require a system integration task. Since Motor Coach Industries (MCI) is the only bus manufacturer that has commercialized their OnGuardACTIVE suite that has many of these components, they still have not integrated all these together with V2X communications to complete a fully automated vehicle. Fortunately, MCIs are the majority of the buses going in to PABT and NJTransit, the largest user of PABT, is in a capital replacement project that will replace all their MCIs by 2020. Most of the MCIs in the last 10 years have implemented the Wabco electronic braking and throttle actuation in their vehicles for stability control. This will make the integration task easier. The integrator will have to work with MCI to implement the

solution so that the warrantees on the existing vehicles are maintained. Some of the systems will be installed on the existing equipment as an aftermarket product and some of them will be installed in the factory since MCI is in the middle of a deployment with NJTRANSIT that will continue until 2020.

The project requires operation in the tunnel entering New York and in the bus terminal in New York. Therefore, the GPS provided with most Connected Vehicle products is not sufficient to provide the location inside the tunnel, on the New York side of the tunnel and inside terminal. The integrator will have to incorporate the Ultra-wideband chip set or some other high accuracy locating technology to make sure the vehicles are located accurately within the lane. Ten (10) cm or better accuracy is preferred due to the narrow lanes in the tunnel.

CHOICE OF SEPARATION POLICY There are many different separation policies being used in the industry. Each vendor has applied their own, often proprietary, algorithms to determine how to fuse together sensor data to come up with an automated control policy.

Google and Tesla are heavily dependent on radar, vision and LIDAR sensors to accomplish the automation tasks.

California PATH has implemented platooning algorithms to combine radar, vision and magnetic nail sensors to automate the vehicle dynamics. They have recently experimented with V2V to maintain the platoon synchronization. They have many focused on a head lead vehicle that is the master with following vehicles as the slave.

Peloton implemented their own proprietary algorithms to do the platooning of trucks using the Wabco actuators. They focused on V2V, radar and vision sensors. This involved a centralized supervisory control that routed nearby trucks to a platoon they could join.

Delphi has a similar supervisory architecture and has combined radar, vision, IR, and LIDAR sensors and has recently added connected vehicle technology.

Robotics deployments have used a Safe Gap force field separation policy to allow each vehicle to receive all the sensory data from many sensors including radar, vision, LIDAR, IR, and V2X to make control decisions.

All these approaches besides the Safe Gap approach use variations of fixed distance headway platooning, constant Time to Collision (TTC), or Intelligent Driver Models (IDM). All algorithms will be considered with the project, but performance requirements will be provided to the vendor that must be met. The risk of these models have been evaluated with various indicator metrics. Some common surrogate indicators are Time To Collision (TTC), inverse Time To Collision (iTTC), Time Exposed Time to collision (TET), Time Integrated Time to collision (TIT), Deceleration Required To Avoid Collision (DRAC), Post Encroachment Time (PET) and Potential Indicator of Collision with Urgent Deceleration (PICUD).

In determining what separation policy to use for the XBL we must consider that the corridor has a mixture of automated and non-automated buses and merge points into the XBL. Most buses have V2X technology incorporated, but there is a need to deal with unknow vehicles and obstacles. The corridor operates a combination of buses, shuttles and vans and they will have a wide range of vehicle dynamics. Most fixed distance platooning algorithms require similar vehicle dynamics to maintain the tight headways and don’t account for varying dynamics. Their performance in a mixed traffic has not been demonstrated. Also, they do not behave well in an environment with a mix of automated and manually controlled vehicles. Manual vehicles cannot merge with these platoons because of the tight headways and would require them to stop until the platoon has passed.

The XBL is a single lane with merge points at the beginning. The vehicles arrive in a random manner and there would be little opportunity to assign platoons at merging time. This would mean that at 50%

penetration you would only get some 2 or 3 car platoons and the lead vehicle would still have to be responsible for manual driving and pulling other vehicles with it. It also does not provide an opportunity for central supervisory systems to assign platoons.

The approaches that allow a higher level supervisory control present a cyber security concern. DSRC has been thoroughly vetted for cyber security and will prevent malicious intrusion from the infrastructure. The supervisory control approaches are likely using cellular data paths to dictate the control instructions which open a security concern.

SAFE GAP FIELD VEHICLE SEPARATION POLICY The “Safe Gap” approach provides a way to integrate connected vehicle technology with off the shelf vehicle technology. These algorithms will be provided to project integrators to use in the deployment to the vehicles.

Safe Gap potential field approach was widely researched in the robotics field in the 80s and early 90s and was first applied to vehicles using connected vehicle technology in 1992 by Robert James. He published the approach widely in the 1990s [1, 2, 3, 4, 5, 6, 7, 8] and was one of the FHWA Precursor System Concept Families funded under the Automated Highway System program. (https://robertdavidjames.files.wordpress.com/2016/12/automated-highway-system-approach.pdf) It was the first approach that suggested using connected vehicles to assist the onboard sensors to automate vehicles. Now that the importance of communication has been recognized in the automation task this approach is being revived to apply to automated projects.

The application of artificial potential/kinetic fields to mobile robot path planning was first introduced by Khatib[9]. He stated the philosophy of the potential field approach as follows:

“The manipulator moves in a field of forces. The position to be reached is an attractive pole for the end effector and obstacles are repulsive surfaces for the manipulator parts.”

With this philosophy in mind, he developed the artificial potential field concept and applied for a real-time obstacle avoidance for manipulators and mobile robots. Once this field concept was introduced, it drew a great deal of attention from people in the field. Warren used this artificial field concept to plan the collision-free paths for multiple robots. To do this, the robots are assigned priority. A path of the highest priority robot is planned to avoid the stationary obstacles first. Then, a trajectory for the next lower priority robot is planned so that it avoids both stationary obstacles and the higher priority robot which is treated as a moving obstacle. Tilove[10] made an overview of the method of the artificial potential field, described the common variations in a unified framework and compared the performance of the different algorithms. Chuang extended this potential field concept of three-dimensional workspace for path planning. It was assumed that the workspace boundary is uniformly distributed with generalized charge. The potential at a distance from a point charge is inversely proportional to the distance to the power of an integer. He claimed that this approach completely eliminates the possibility of the collision between two objects. Masoud and Bayoumi[11] extended this potential field concept to the Biharmonic potential fields which govern mechanical stress fields in homogeneous solids. Keymeulen and Decuyper[12] proposed a new method that generates a collision free-path from a vector field which is not necessarily the gradient of a potential function. Their approach consists of representing the robot’s work space as a pipe-system with a fluid pump installed at the robots’ initial position and an outlet at its destination. The robot is simulated as a fluid particle that moves through the pipe-system under the action of the pump. Makita, Hagiwara and Nakagawa[13] use this artificial potential field along with fuzzy rules to plan a collision-free path for a truck backing up problem. Kitamura, Tanaka, Kishino and Yachida[14] proposed that the robots and environments should be represented by using an octree without any distinction of movability of objects. A potential field is generated for each cell of the octree. Thus, the potential field at any point in the environment is given by the maximum of the potentials due to individual cells. This

prevents local maxima of the potential field from appearing in free space. Nam, Lee, and Ko[15] proposed a unified method that incorporates the artificial potential field concept into view-time based motion planning, where the driving force is generated at every interval of the view-time. The view-time is defined as the time set from one sampling time instant to the next. Hennessey, Shankwitz and Donath[16] proposed a 2-degree of freedom strategy based on the concept of a virtual bumper. Their approach is based on surrounding the perimeter of the vehicle with a sensor-based computer controlled bumper. As the bumper’ boundary is deflected, a virtual force proportional to the amount of deflection is generated. The vehicle controller responds to this virtual force in such a way as to return the bumper to its non-deflected state.

Interest in “Safe Gap” fields for vehicle flow modeling was renewed as researchers realized that connected vehicles would allow vehicles to exchange their information and that information beyond the preceding vehicle would be available to the following vehicles. Ni[17] in 2013 eloquently described the application of fields to vehicle flow modeling and described vehicles traveling through virtual field valleys created by all the moving and non-moving objects and lane lines around them. In 2014 to the present, Chinese researcher Wang[18, 19] et., al. applied the potential and kinetic fields to vehicle following models and described how field flow modeling allowed simultaneous longitudinal and lateral control and allowed for smooth merging and transition behavior compared to previous vehicle following models. They even conducted live vehicle studies in China producing qualitative and quantitative results demonstrating the effectiveness of the safe gap field approach. Netherland researchers Babu & Arem[20] at University of Technology, Delft did a comparison of iTTC, PET, PICUD, warning Index and Safety field flow models and showed how the safe gap field approach behaves continuously with merges, cut-ins and lane change scenarios where other models are discontinuous.

All this prior research has demonstrated the potential solution of collision-free path using the artificial force field approach. All this previous work has motivated us to develop an artificial force field approach based on combined kinetic and potential fields created by all the objects surrounding a vehicle. These fields will allow the vehicles to find a path of least resistance to guide an automated vehicle with collision-free motion. The field will be generated by the infrastructure, with a field generator, for each vehicle and environment. The field generator uses the parameters from the estimator to generate fields for each vehicle. These fields in general will be nonlinear functions of these parameters. This field will be a dynamic, real-time field for each vehicle because it will be a function of the system dynamic parameters, the road-tire parameter, etc. The roadway and obstacles also generate fields as shown in Figure 1A. When there is an emergency situation, this field will change in order to force the vehicle to respond in a particular way. The fields are generated so that the system is optimized for safety given the desired throughput and actual traffic. For instance, using the field generation and calculation, an initial configuration of Figure 1(a) will be converted to the one shown in Figure 1(b) which will be safer for emergency situations, and especially when there is not much traffic demand, this scenario will provide the same throughput also.

The artificial potential fields for generating the configurations for the vehicles are being developed. These fields should have the following properties:

1. The field function is continuous.

2. The fields consist of kinetic and potential elements. The strength of a field decreases as the distance to the object which generates that field increases. The strength of the field will also depend on other

parameters. For example, the kinetic strength will depend on relative velocity so that a stopped vehicle will have a larger field strength than a moving vehicle. These will be adapted to the relative braking dynamics of the vehicles and road conditions. Contributors to the potential fields include the uncertainty of the behavior of the motion of the object and its inability to yield to the vehicle. A pedestrian or bike has a certain ability to make sudden motions that are accounted for by the potential field they create. Lane lines with a shoulder will have a softer force than ones with a hard barrier at the edge.

3. Each vehicle will have a driving field to provide the desired velocity in the desired direction for that vehicle. This driving field will exert a force only on that vehicle in order to keep it moving forward. In other words, the vehicle will be affected by its own driving field, not by the other driving fields. This driving field can be overcome by the potential field of the obstacle, and thus the driving field will not drive a vehicle into an obstacle.

Initial analysis shows a nonlinear relationship based on ignoring vehicle dynamics and considering different deceleration capabilities of vehicles and different reaction times. The plots are shown in Figure 2.

Figure 1A The Potential Field Generation for a Scenario

Some researchers have attempted to make these forces additive, but reason would indicate that the largest force acting on the vehicle in each direction should govern the vehicles response. For instance, assume a vehicle two cars ahead has a large V that produces a large force on the following vehicles that exceeds the immediately preceding vehicle’s force. The following vehicle should use this force to dictate their response instead of the one created by the immediately preceding vehicle. There is no reason to add the forces. So a MAX function of the forces being contributed from each source laterally and longitudinally should be used.

CONTROL COMMANDSThe commands for the longitudinal and lateral spacing of the vehicles are generated by solving the field equilibrium problem. The problem statement is: given an initial configuration, maximize the safety of the system by using minimum motion to produce the desired configuration state. These commands are then passed onto the vehicle controller, which processes them in its own feedback control to achieve the desired motion.

The algorithms to control the vehicle behavior will be based on non-linear frame of reference. The longitudinal position of vehicles and object will be relative to their distance along the lane center lines based on a perpendicular line to the lane center lines. Similarly, the lateral location will be the distance along the perpendicular line to the lane center line. This will take into account the road curvature along the path traveled by the vehicles and anticipate interactions along that line. Merging lanes will have lane center lines that intersect at some point. Relative interactions between merging vehicles will begin when force vectors are felt based on relative spatial and velocity vectors.

Parameters that will affect the vehicles flow will be based on the driving force function and the resistive forces that are generated from the vehicles, objects, and lane lines based on relative dynamics. Not all vehicles will have actuation and not all objects detected will be cooperatively communicating their information. This will produce an uncertainty function that will be larger or smaller based on the degree of cooperation and the ability to automatically actuate the vehicle.

Driving ForceTypically, the driving force will be relative to the posted speed limits and current vehicle speed. The drivers will be able to set an “Aggression” Parameter based on a button that allows them to adjust the following based on their desired comfort and desire to maintain efficiency. This will only provide 3 or 4 levels of adjustment to maintain the automated efficiencies and safety limitations.

Vehicle ForcesVehicles will exert a longitudinal force based on the relative vehicle dynamics, relative velocities, relative distance, object uncertainty, and relative alignment from lane center. Lateral forces are coupled with longitudinal forces in that potentials for adjacent lane vehicles to move into the subject lane create a potential longitudinal force on the following subject lane in addition to the expected lateral force vector. A lateral V by the encroaching vehicle can create an even larger longitudinal component in the subject lane. This will allow natural gradual separations for merging vehicles that will not abruptly disrupt the flow behavior.

Vehicle Dynamics - The ability of a vehicle to brake and accelerate can vary widely with different vehicle types and vehicle loads. The V2V will provide the vehicle dynamics information. Current standards only provide vehicle type. However, the quality of maintenance and the current load of the vehicles could impact the vehicle’s dynamic capabilities. We intend to have each vehicle maintain its own braking and acceleration envelopes in real-time by comparing the relative speed and location data from the GPS, gyro, and odometer combined measurements with the CAN bus obtained throttle and braking data. This dynamic data will be maintained in two time-smoothed metric parameters to be shared with surrounding vehicles. Similar parameters for lateral steering will be shared as well. (Not only will these real-time

statistics be used to calculate vehicle forces, but they will be shared to evaluate the vehicle’s maintenance and health along with other CAN bus provided data.)

Relative Velocities – All the sensors can provide V metrics to varying degrees in various situations. It is anticipated that these will be combined in a Kalman Filter to obtain the most appropriate Vs for all vehicles in the path of travel. Radar, Vision and LIDAR can pick up non-cooperative objects in the field of view, but are limited to line of site. Connected vehicles can communication with vehicles beyond line of site and anticipate actions not detected with the other sensors. This will provide faster more smooth response to upstream events. Following vehicles will exert forces on preceding vehicles to potentially speed them up to avoid rear end collisions and leading vehicles will exert forces on following vehicles to potentially slow their progress. With connected vehicles, there could potentially be dozens of vehicles along the path of travel contributing to the vehicle responses.

Relative Distance – The relative distance to the objects along the path of travel will be coupled to the relative velocity of the objects to determine the relative force of the object on the actuated vehicle. There is a minimum following distance that needs to be maintained based upon the maximum braking capabilities of the two vehicles and the sensor maximum response time to a sudden deceleration of the lead vehicle. An uncertainty and comfort factor should be added to this value.

Object Uncertainty – The objects that could potentially obstruct the path of travel have varying uncertainties that could potentially significantly affect the force vectors exerted. A connected vehicle has the least uncertainty since its behavior and dynamics are well known. We may have some ability to detect pedestrians with connected vehicle technology as is being demonstrated in Tampa with their use of their cell phones. However, pedestrians and non-connected vehicles will primarily be detected through the radar and vision sensors. The type of object can be important to its uncertainty as well. A vehicle has limited ability to change lanes quickly and exert a large lateral or longitudinal V, but with a slower response time from these sensors the force vector is somewhat larger than that of a connected vehicle. A pedestrian could change directions at any time and become an obstacle in the path of travel. Their lateral uncertainty could impact the flow significantly. On a dedicated facility, the occurrence of this is not frequent, but it is very important to be able to reliably anticipate this scenario. Degraded visibility conditions for these sensors could have an impact on the Driving Force so a lower set speed is used to lower the V.

Relative Alignment from Lane Center – The width of vehicles and their alignment relative to lane center will impact the strength of the longitudinal and lateral forces exerted. As a vehicle is pushed to the side by the forces being generated there will come a point where the vehicle envelopes will not intercept as the relative distance approaches zero. The lane lines will exert a force of various magnitudes to counter the lateral forces between vehicles. Depending on the geometry the vehicles may or may not be able to achieve enough lateral separation to avoid each other if the relative distance approaches zero. If not, then the force between vehicles will prevent any further change in distance (potentially bringing the following vehicle to a stop).

Object ForcesObjects detected by the sensors will exert a longitudinal force based on the relative velocity and distance to the object and the uncertainties of the object behavior. Classification of the objects can assist in determining the force impacts they can be expected to have. Vision sensors can do a good job of classification of non-cooperative objects, but LIDAR can be added to further improve this classification and help anticipate expected behavior. Radar, Vision and LIDAR sensor are limited to line of site so very small headways impair the ability of these sensors to pick-up non-cooperative objects in the path of travel. Object detection from preceding connected vehicles may be able to pre-load objects to be considered to following vehicles. However, this requires a significant percentage of connected vehicles

to become useful. The force vectors exerted by objects is described above in the Vehicle Forces section.

Lane Line ForcesLane line forces are lateral forces exerted on vehicles to push them to the center of the lanes. In the absence of other forces the vehicle will keep lane center as it drives down the lane. The presence of shoulders and center lines will allow vehicles to pass the lane barrier if enough other forces are experienced. However, hard barriers at the edge of the lane will exert extremely strong lateral forces preventing vehicles from passing the line. The pinching effect of a vehicle trying to pass between an immovable object and a hard barrier could cause a longitudinal slow down since no lateral solution exists.

Lane lines determine the expected vehicle path of travel in the near future. Vision sensors can detect lane lines when all conditions are favorable, but there are many situations (snow cover, poor lane markings, heavy rain, dirty windshield, etc.) where these lane lines are not detected with vision sensors. LIDAR can only pick up reflective markers if deployed in the roadway. Connected Vehicles provide some knowledge of lanes through map matching and tracking the path of the preceding vehicles. With open sky environments, the accuracy of 1m can provide some relative lane positions when no vision sensor is available. Ultrawideband (UWB) sensors can be deployed along the right of way to very accurately (<5cm) communicate the lane positions. This is especially useful in tunnel and urban canyon environments when no GPS is available. It is also useful in work zones where map matching may not be accurate.

SENSOR INPUTSThe automation of the vehicles will consider multiple inputs to determine the longitudinal and lateral control of the vehicles. Each sensor will provide longitudinal and/or lateral contribution to the parameters that are used in the potential force calculations. The uncertainty associated with each sensor input will be factored in to their contribution to the parameters they measure. Multisensor fusion is key to the establishment of a zero-fatality policy. Sensors fail in various scenarios and for mechanical reasons. Each sensor also has capabilities that effect it’s accuracy and reliability in various scenarios.

Sensor

Forward Looking Radar (Range and V to dominant object in field of view)

Beam 15 Horizontal - 4 VerticalRange – 200mAccuracy – 10 cmV – 0.1 m/s

Vision Sensor (Headway, Warnings, and Sign Recognition)

Angle of View - 38 HorizontalHeadway(sec) – 0 to 10 seconds (0.1s increments)Warnings

o Pedestrian in Danger Zone, o Left/Right Lane Departure, o Headway Warnings at 2 configured settingso Speed Limit Warnings at 5mph increments (up to 35mph) over posted road speed

Sign Recognition

LIDAR Sensor (Range & Object Classification)

Angle of View - 360 Horizontal, 30 Vertical

Angular Resolution – 0.4Range - 100m w 3cm accuracy (Limited to direct line of site)Calibrated Reflectivity MeasurementsData Points – 300,000 per second

V2X (Relative Vehicle Locations & Data Communications)

GPS Accuracy – 1m (open sky) to >50m (Urban canyon)Range - 300mCommunication – 75MHz band for data to share vehicle and infrastructure messages – Basic Safety Message at 10 per second

Ultrawideband (Relative Vehicle Locations & Data Communications)

Relative Location Accuracy – 1cm to 5cm (direct line of site per pairs of units, but shared with others over Communication channel for a networking effect)Range - 200mCommunication – 100 kbits for data (V2I to pass perpendicular distance to lane lines and force vector)

Automobile sensors have become relatively common items with the exception of LIDAR sensors. Current price points of $8,000 for low end units have made them somewhat expensive for large scale deployment. While they are important in making autonomous driving decisions in a complex urban driving environment, in a dedicated highway environment they probably do not provide the benefit needed to justify their expense. However, other sensors are under $1k and for extremely large quantities can be made under $100 per vehicle.

Ultra-wideband technology has been around for more than 20 years in defense applications and more recently in mining applications where GPS signals are not available. This technology can provide centimeter accuracy for V2V and V2I applications. This will be necessary in large cities where urban canyon effects the GPS accuracy and tunnel and mountain areas where satellites are not available. Deployed vehicles should work in all environments and scenarios to be considered reliable.

SCENARIO DEPENDENCY OF SENSORSEach sensor has strengths and weaknesses in various situations. Most vehicle automation testing has been done in ideal conditions. Many of the fully automated vehicles based solely on onboard sensors fail to operate in adverse weather conditions or poor lane marking conditions. The industry has come to understand that vehicles need to be connected to each other and to the infrastructure in order to become fully autonomous zero-fatality vehicles.

Below you can see what the strengths and weaknesses are for each type of sensor for Longitudinal, lateral, and intersection control. As can be seen the V2X technology fills in many holes in the scenarios represented that onboard sensors have difficulty operating. However, some have come to realize that due to the weakness of GPS in urban canyon and tunnel environments that additional technology is still needed for continuous operation. Ultra-wideband technology seems to be a good choice to fill in these last operational holes.

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References[1] James, Robert D., “An Infrastructure Controlled Approach to Automated Highways”, publication and presentation in the 1995 ITS America Annual Meeting proceedings.

[2] James, Robert D., Sampan, Somkiat, “Vehicle Classification of Acoustic Signals Using Neural Networks”, publication and presentation in the 1995 ITS America Annual meeting proceedings.

[3] James, Robert D.; Mendola, Jeffrey B., “Comparison of Deployment Strategiesfor Maximizing AHS Capacity” publication and presented at ITS America 1996 Annual Meeting proceedings.

[4] James, Robert D.; Mendola, Jeffrey B., “Ultra-wideband (UWB) Communication Technology Applications in Intelligent Transportation Systems(ITS)” publication and presented at ITS America 1996 Annual Meeting proceedings.

[5] James, Robert D.; Mendola, Jeff “Ultrawideband Technology for Vehicle to Vehicle Communications and Sensing” presentation at SPIE on October 23-25, 1995.

[6] James, Robert D.; Mendola, Jeff “Ultrawideband Technology for Range-finding and Communications” presentation at SPIE on October 23-25, 1995.

[7] James, Robert D.; and others, “Neural Network Techniques for Multistatic Hyperbolic Vehicle Positioning” publication and presented at the Third Annual World Congress on Intelligent Transportation Systems.

[8] James, Robert D.; Hannan, Anwarul “Summary of Position Location Technologies for Mayday and Other ITS Applications.” Presentation and publication for the proceedings of the 1997 Annual meeting of ITS America.

[9] Khatib, Oussama, “Real-time Obsticle Avoidance for Manipulators and Mobile Robotics.” http://cs.stanford.edu/group/manips/publications/pdfs/Khatib_1986_IJRR.pdf

[10] Tilove, R. B., 1990, "Local Obstacle Avoidance for Mobile Robots Based on the Method of Artificial Potentials." To be presented at the 1990 IEEE International Conference on Robotics and Automation, Cincinnati, Ohio, May 13-18, 1990.

[11] A. Masoud, Samer A. Masoud, Mohamed M. Bayoumi, "Robot Navigation Using a Pressure Generated Mechanical Stress Field, The Biharmonic Potential Approach", The 1994 IEEE International Conference on Robotics and Automation, May 8-13, 1994 San Diego, California, pp. 124-129.

[12] J. Decuyper and D. Keymeulen, “A reactive robot navigation system based on a fluid dynamics metaphor,” in Proc. Parallel Problem Solving From Nature, First Workshop, H. Schwefel and R. Hartmanis, Eds., Dortmund, Germany, Oct. 1–3, 1990, pp. 356–362.

[13] Makita, Y.; Hagiwara, M. and Nakagawa, M., (1994) ”A simple path planning system using fuzzy rules and a potential field”, Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence, Proceedings of the Third IEEE Conference on, 1994, Page(s): 994 -999 vol.2

[14] Y. Kitamura, T. Tanaka, F. Kishino, and M.Yachida. Real-time path planning in a dynamic 3-d environment. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 1996.

[15] B. H. Lee Y. S. Nam and N. Y. Ko, "An analytic approach to moving obstacle avoidance using an artificial potential field", in Proceedings 1995 IEEE International Conference on Intelligent Robots and Systems. Human Robot lnteraction and Cooperative Robots, 1995, vol. 2, pp. 482- 487.

[16] Hennessey, M., C. Shankwitz and M. Donath (1995). Sensor based ‘virtual bumpers’ for collision avoidance: Configuration issues. In: Proceedings of the SPIE. Vol. 2592.

[17] Ni, D., 2013. A unified perspective on traffic flow theory, part I: the field theory. Appl. Math. Sci. 7 (39), 1929–1946.

[18] Wang, J., Wu, J., Li, Y., Li, K., 2014. Concept and modeling of driving safety field based on driver-vehicle-road interactions. IEEE 17th ITSC, 974–981.

[19] Jianqiang Wang a,⇑, Jian Wua, Xunjia Zheng a, Daiheng Ni b, Keqiang Li, 2016. Driving safety field theory modeling and its application in pre-collision warning system. Transportation Research Part C 72 (2016) 306–324

[20] Mullakkal Babu, Wang, Farah, van Arem, Happee, 2017. Comparative Assessment of Safety Indicators for Vehicle Trajectories on Highway. TRB Annual Meeting