Experience simulating the safety impacts of commercially available connected vehicle technology.
Nationwide, United States
The Real-World Safety Potential of Connected Vehicle Technology
Summary Information
This article estimated the safety potential of connected vehicle autonomous brake assist technology. Crash data from on-scene investigations in South Australia were used to reconstruct and simulate real-world crashes. A total of 89 crashes were selected for inclusion in the study. The crashes selected represented the most prevalent crash types for injury or fatal crashes with potential to be mitigated by connected vehicle systems. The trajectory, speeds, braking, and impact configuration documented in each case study were replicated in a software package and converted to a file format allowing the scenario to be replayed in real-time to assess the function of onboard units (Cohda Wireless MK2) configured to receive basic safety messages (BSMs) at 10 messages per second from errant vehicles and activate in-vehicle warnings and automated braking features when needed. The crash replay was achieved by replacing each of the onboard unit GPS inputs with the simulated data of each of the involved vehicles. The time at which the in-vehicle threat detection software issued an elevated warning was used to calculate a new impact speed using three different braking reaction scenarios (autonomous braking, and 1.2 s and 0.7 s reaction time braking for drivers receiving in-vehicle warnings) and two braking deceleration levels (0.4 g and 0.7 g).
RESULTS
Between 37 percent and 86 percent of the simulated crashes could be avoided, with the highest percentage due to autonomous system braking at 0.7 g. The same system also reduced the impact speed relative to the actual crash in all cases. Even when a human reaction time of 1.2 s and moderate braking of 0.4 g was assumed, the impact speed was reduced in 78 percent of the crashes.
The findings below were calculated from actual crash data reports (n=89) input into the simulation model.
 |
Braking level (g) | Actual crash data | 1.2 s reaction Number |
1.2 s reaction Percentage |
0.7 s reaction Number |
0.7 s reaction Percentage |
Autonomous Number |
Autonomous Percentage |
| Impact speed reduced | 0.4 | - | 69 | 77.5 | 73 | 82.0 | 81 | 91.0 |
| Impact speed reduced | 0.7 | - | 77 | 86.5 | 83 | 93.3 | 89 | 100.0 |
| Crash avoided | 0.4 | - | 33 | 37.1 | 43 | 48.3 | 53 | 59.6 |
| Crash avoided | 0.7 | - | 51 | 57.3 | 68 | 76.4 | 77 | 86.5 |
| Average impact speed (km/h) | 0.4 | 59.3 | 35.2 | - | 27.5 | - | 17.0 | - |
| Average impact speed (km/h) | 0.7 | 59.3 | 21.6 | - | 12.4 | - | 4.4 | - |
Note: The average initial crash speed was 72.3 km/h, meaning that drivers without any connected vehicles systems were able to reduce their speed by an average of 13 km/h before impact.
These results indicate that connected vehicle technology can be greatly beneficial in real-world crash scenarios and that this benefit would be maximized by having the vehicle intervene autonomously with heavy braking. Crash types that proved most difficult for the on-board threat detection engine to mitigate were head-on crashes where the approach angle was low and right turn–opposite crashes.
Note: The sample of 89 crashes included in this study were selected to be representative of the most prevalent crash types for injury or fatal crashes that had potential to be mitigated by connected vehicle technology. The data were limited to specific crash types and speed zone, thus not all crashes were represented.
