The perils of perfect performance; considering the effects of introducing autonomous vehicles on rates of car vs cyclist conflict

Figures & data

Figure 1. Depiction of the dynamic relationship between agents, their actions, the environment, consequences for the agent and consequences for the agents’ state.

Figure 2. Top-down, angled view of the Netlogo model showing the position of vehicles and vehicle classes represented in the system.

Table 1. Factors, notation, and settings associated with each trial under each experimental condition.

Factor	Notation	Settings
Saliency (road)	$\alpha$	1.0
Saliency (AV to cyclist or bicycle to driver)	${\beta }_i$	0.6, 0.8, 1.0 (SD = 0.1)
Care	${\omega }_i$	0.6, 0.8, 1.0 (SD = 0.1)
Capacity	${\sigma }_i$	0.6, 0.8, 1.0 (SD = 0.1)
Memory span (time steps)	$m$	15, 30, 45

Figure 3. Pseudo-code associated with behaviour of cyclists (Block 1) and cars (Block 2) at intersections as it relates to crash-risk.

Figure 4. Mean count of car vs cyclist conflicts per time-step with additional deployment of AVs or Manual cars at time-step 100.

Figure 5. (a) Mean count of car vs cyclist conflicts per time-step with additional deployment of 500 AVs at time-step 100 under each of 3 AV saliency conditions, 0.6, 0.8, and 1.0. (b) Mean association between AVs and the road environment recorded for simulated cyclists per time-step with additional deployment of 500 AVs at time-step 100 under each of 3 AV saliency conditions, 0.6, 0.8, and 1.0.

Figure 6. Mean count of car vs cyclist conflicts per time-step with additional deployment of (a) 500 AVs at time-step 100 under each of 3 memory span conditions of 15, 30, and 45 time-steps. (b) 500 manually-driven cars at time-step 100 under each of 3 memory span conditions of 15, 30, and 45 time-steps.

Figure 7. Mean count of car vs cyclist conflicts per time-step with additional deployment of (a) 500 AVs at time-step 100 under each of 3 bicycle saliency conditions, 0.6, 0.8, and 1.0. (b) 500 manually-driven cars at time-step 100 under each of 3 bicycle saliency conditions, 0.6, 0.8, and 1.0.

Figure 8. Count of car vs cyclist conflicts per time-step with trade of manually driven cars for AVs at a rate of 1 per time-step over 2000 total time-steps alongside a fitted cubic spline curve and risk of car vs cyclist conflicts per manually driven car.

Figure 9. Mean speed of bicycles per time-step and mean association strength (V) of manually driven cars.

Table

Adapted ODD Protocol (Grimm et al., 2010; Grimm, Polhill, & Touza, 2017)
ODD - Overview, Design concepts, and Details
Purpose of the model
The purpose of the model is to better understand potential patterns of crashes between vulnerable road users (namely cyclists) and cars following the introduction of AVs into a transport system. The model explores changes in car-cyclist crash frequency associated with the introduction of AVs into an existing transport system under conditions where AVs perform ‘flawlessly’, human drivers adapt to the presence of cyclists, and cyclists adapt to the presence of AVs.
Entities, state variables and scales
1. Manually driven cars (Drivers)
Manually driven cars possess states of speed, location, heading, memory span, perception for bicycles, perception for the roadway, levels of care to drive safely, capacity to drive safely, and association between cyclists and their presence on the road shared by the driver. Manually driven cars have capacity to perceive other vehicles located in their direct path.
2. Autonomous Vehicles (AVs)
AVs possess states of speed, location, and heading. They have the capacity to perceive other vehicles located in their direct path.
3. Cyclists
Cyclists possess states of speed, location, heading, memory span, perception for autonomous vehicles, perception of the roadway, perception of manual cars, level of care to ride safely, capacity to ride safely, association between autonomous vehicles and their presence on the road shared by the rider. Cyclists have capacity to perceive other vehicles located in their direct path and also perceive the number of other cyclists surrounding them in a 1 unit radius.
4. Roads
Roads are locations where vehicles travelled between intersections and can perceive one another but they cannot cross paths and cannot turn nor crash.
5. Intersections
Intersections are locations between roads where vehicles compete for space in order to continue their path. Crashes only occur at intersections.
Neither time nor distance in the model are scaled to represent real-world units.
Process overview and scheduling
Initialisation
At time step 0 (initialisation), the layout of the system is established including roads, intersections and placement of vehicles on empty roads and intersections in the network.
State variables associated with each vehicle class listed above are allocated to individual cyclists, manual cars and autonomous vehicles including initial location, speed, heading, association, and memory span.
Model Processing
Vehicle Class updates
At each time step, the following actions occur for members of each vehicle class:
All vehicles – Determine heading Update location Update speed Register presence of other at distance of 1 unit ahead Drivers – Calculate time since last observing a cyclist on the road Update level of association between cyclists and roadways Update likelihood of potential for collisions with cyclists given an interaction at current time-step Record collisions with cyclists Cyclists – Calculate time since last observing an AV on the road Update level of association between AVs and roadways Register presence of any manually driven cars with positive collision potentials at current location System Updates – Calculate total numbers of all vehicle classes in the system Calculate total numbers of collisions in the system Introduce new AVs into the system (Transfer condition – Experiment 2) Remove manually driven cars from the system (Transfer condition) If timestep = 250, deploy 500 AVs or manually driven cars into the system (Deployment condition – Experiment 1)
Output
Production of graphics and charts in user interface for interpretation Production of .csv datafile recording number of vehicles in each class, collisions, and levels of awareness among cyclists and drivers at each step for quantitative analysis
System Design	System Units
Area:
Length	130 units
Width	130 units
Area (individual patches)	16900 units
Intersections	4356
Agents:
Initial bicycles	500
Initial manually driven cars	2000
Initial autonomous vehicles	0
Speed per time-step:
Max speed of cars (manual and AV)	1.0 velocity unit
Max speed of bicycles	0.3 velocity units
Minimum speed of cars	0 velocity units
Minimum speed of bicycles	0.05 velocity units
Distance:
Distance units	1
Association:
Level of individual association among cyclists and drivers	Between 0 (minimum association) and 1 (maximum association)
Memory Span:
Number of time units each driver is able to remember observing a bicycle for and time each cyclist is able to remember seeing an AV for prior to association beginning to extinguish (m)	15, 30, or 45 time steps
Awareness:
Individual driver awareness of bicycles on the road (time since last observation of a cyclist < driver memory span)	Boolean (1 = aware, 0 = unaware)
Road saliency:
Extent to which drivers and cyclists consider paths travelled on by them to be ‘shared’ with cyclists and AVs, respectively (α)	Between 0 (not shared) and 1 (always shared) – set to 1 at all times
Bicycle Saliency:
Extent to which bicycles on the road are observed by drivers (β)	Between 0 (not observable at all) and 1.0 (always observed)
Care:
Individual drivers’ intention to drive safely among cyclists (ω)	Between 0 (no intention) and 1 (total intention)
Capacity:
Individual drivers’ capacity to drive safely around cyclists (θ)	Between 0 (no capacity) and 1 (total capacity)
Time:
Total time-step units elapsed (tk)	200 time units (Deployment condition)
	2000 time units (Transfer condition)
Change in vehicle class count per time step – Deployment condition:
Experiment a: Change in AV count	+500 at time-step 100
Experiment b: Change in manually driven car count	+500 at time-step 100
Change in vehicle class count per time step – Transfer condition:
Change in AV count	+ 1 per time step
Change in manual car count	−1 per time-step
Heading:
Direction of travel for vehicles	North, South, East, West
Exploration factor:
Likelihood that a vehicle of any class will change direction at any intersection	5%

Grimm, V., U. Berger, D. L. DeAngelis, J. G. Polhill, J. Giske, and S. F. Railsback. 2010. “The ODD protocol: a review and first update.” Ecological Modelling 221 (23): 2760–2768. doi:10.1016/j.ecolmodel.2010.08.019.

 Web of Science ®Google Scholar

Grimm, V., G. Polhill, and J. Touza. 2017. “Documenting social simulation models: The ODD protocol as a standard.” In Simulating Social Complexity, 349–365. Springer.

 Google Scholar