Billboard
Performance billboard across Safety, Mobility, User Comfort, and Energy-efficiency.
Results
Overall Ranking
Overall performance radar chart across the four evaluation dimensions.
Brand and dataset metadata used by the ranking.
| Brand | Model | Year | Dataset | Supplementary Note |
|---|---|---|---|---|
| Tesla | N/A | N/A | Central Ohio ACC | The tested Tesla has been retrofitted (Xia et al., 2023). |
| Toyota | Corolla LE | 2020 | MicroSimACC | - |
| Lincoln | MKZs | 2016 | CATS ACC | - |
| N/A | N/A | 2019 | Vanderbilt ACC | Unknown commercial SUV (Wang et al., 2019). |
| KGM | Rexton | N/A | OpenACC-Casale | - |
| Ford | S-Max | 2018 | OpenACC Vicolungo | - |
| Mazda | 3 | 2019 | OpenACC Asta | - |
| BMW | X5 | 2018 | OpenACC ZalaZone | - |
References
- Xia, X., Meng, Z., Han, X., Li, H., Tsukiji, T., Xu, R., Zheng, Z., Ma, J., 2023. An automated driving systems data acquisition and analytics platform. Transportation Research Part C: Emerging Technologies 151, 104120.
- Wang, Y., Gunter, G., Nice, M., Work, D.B., 2019. Estimating adaptive cruise control model parameters from on-board radar units. arXiv preprint arXiv:1911.06454.
- Zegeye, S. K., De Schutter, B., Hellendoorn, J., Breunesse, E. A., Hegyi, A., 2013. Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes. Transportation Research Part C: Emerging Technologies 31, 158-171.
Safety Ranking
Safety is evaluated using Time-to-Collision (TTC). The columns in the table represent TTC intervals, ordered from the collision state to increasingly safer conditions.
TTC ranges probability by model.
| Rank | Model | [0,0] | (0,0.25] | (0.25,0.75] | (0.75,1.5] | (1.5,3] | (3,5] | (5,8] | (8,∞) |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Toyota Corolla LE (2020) | 0.0E+00 | 0.0E+00 | 0.0E+00 | 0.0E+00 | 2.3E-07 | 1.3E-04 | 3.1E-03 | 1.0E+00 |
| 2 | KGM Rexton (N/A) | 0.0E+00 | 0.0E+00 | 0.0E+00 | 2.1E-06 | 2.4E-04 | 1.1E-03 | 1.1E-02 | 9.9E-01 |
| 3 | Lincoln MKZs (2016) | 4.2E-08 | 9.9E-05 | 1.1E-04 | 2.2E-04 | 4.4E-04 | 8.2E-04 | 3.9E-03 | 9.9E-01 |
| 4 | Tesla (N/A) | 2.0E-06 | 7.4E-05 | 8.3E-05 | 1.8E-04 | 6.3E-04 | 3.1E-03 | 3.2E-02 | 9.6E-01 |
| 5 | Mazda 3 (2019) | 1.4E-06 | 4.1E-05 | 2.0E-04 | 4.5E-04 | 1.3E-03 | 2.7E-03 | 2.0E-02 | 9.8E-01 |
| 6 | BMW X5 (2018) | 6.5E-06 | 1.1E-04 | 3.5E-04 | 8.5E-05 | 2.6E-05 | 1.2E-03 | 4.0E-01 | 6.0E-01 |
| 7 | N/A (2019) | 8.9E-06 | 1.5E-03 | 1.9E-03 | 2.0E-03 | 3.5E-03 | 2.0E-02 | 8.3E-02 | 8.9E-01 |
| 8 | Ford S-Max (2018) | 2.6E-05 | 7.5E-04 | 1.0E-03 | 1.1E-03 | 1.2E-03 | 2.6E-03 | 2.2E-02 | 9.7E-01 |
Brand and dataset metadata used by the ranking.
| Brand | Model | Year | Dataset | Supplementary Note |
|---|---|---|---|---|
| Toyota | Corolla LE | 2020 | MicroSimACC | - |
| KGM | Rexton | N/A | OpenACC-Casale | - |
| Lincoln | MKZs | 2016 | CATS ACC | - |
| Tesla | N/A | N/A | Central Ohio ACC | The tested Tesla has been retrofitted (Xia et al., 2023). |
| Mazda | 3 | 2019 | OpenACC Asta | - |
| BMW | X5 | 2018 | OpenACC ZalaZone | - |
| N/A | N/A | 2019 | Vanderbilt ACC | Unknown commercial SUV (Wang et al., 2019). |
| Ford | S-Max | 2018 | OpenACC Vicolungo | - |
Ranking Method
Safety is quantified using Time-to-Collision (TTC), a standard rear-end risk indicator. At time t, TTC is the remaining time to collision assuming current speed and gap remain unchanged. Larger TTC indicates safer conditions, while smaller TTC indicates higher risk.
In the aggregated Markov evaluation framework, TTC is discretized into hazard-prioritized blocks:
Let \( \pi_i^k \) be the probability that PAV \( i \) operates in TTC block \( k \). We define a transformed vector \( \mathbf{z}_i \) with:
PAVs are ranked using lexicographic comparison on \( \mathbf{z}_i \) from the most hazardous block to the least hazardous block. This means we first compare collision-state probability, then proceed block-by-block only when ties occur. Under this rule, models with larger probability mass in safety-critical TTC blocks receive lower ranks.
References
- Xia, X., Meng, Z., Han, X., Li, H., Tsukiji, T., Xu, R., Zheng, Z., Ma, J., 2023. An automated driving systems data acquisition and analytics platform. Transportation Research Part C: Emerging Technologies 151, 104120.
- Wang, Y., Gunter, G., Nice, M., Work, D.B., 2019. Estimating adaptive cruise control model parameters from on-board radar units. arXiv preprint arXiv:1911.06454.
Mobility Ranking
Mobility is measured by average time headway. Smaller time headway implies higher roadway capacity and better mobility.
Mobility ranking results by model.
| Rank | Model | Time Headway (s) |
|---|---|---|
| 1 | BMW X5 (2018) | 0.909 |
| 2 | Toyota Corolla LE (2020) | 0.975 |
| 3 | Ford S-Max (2018) | 1.007 |
| 4 | Mazda 3 (2019) | 1.211 |
| 5 | N/A (2019) | 1.370 |
| 6 | KGM Rexton (N/A) | 1.390 |
| 7 | Lincoln MKZs (2016) | 1.669 |
| 8 | Tesla (N/A) | 1.721 |
Brand and dataset metadata used by the ranking.
| Brand | Model | Year | Dataset | Supplementary Note |
|---|---|---|---|---|
| BMW | X5 | 2018 | OpenACC ZalaZone | - |
| Toyota | Corolla LE | 2020 | MicroSimACC | - |
| Ford | S-Max | 2018 | OpenACC Vicolungo | - |
| Mazda | 3 | 2019 | OpenACC Asta | - |
| N/A | N/A | 2019 | Vanderbilt ACC | Unknown commercial SUV (Wang et al., 2019). |
| KGM | Rexton | N/A | OpenACC-Casale | - |
| Lincoln | MKZs | 2016 | CATS ACC | - |
| Tesla | N/A | N/A | Central Ohio ACC | The tested Tesla has been retrofitted (Xia et al., 2023). |
References
- Xia, X., Meng, Z., Han, X., Li, H., Tsukiji, T., Xu, R., Zheng, Z., Ma, J., 2023. An automated driving systems data acquisition and analytics platform. Transportation Research Part C: Emerging Technologies 151, 104120.
- Wang, Y., Gunter, G., Nice, M., Work, D.B., 2019. Estimating adaptive cruise control model parameters from on-board radar units. arXiv preprint arXiv:1911.06454.
User Comfort Ranking
User comfort is evaluated using squared acceleration. Larger acceleration variations indicate lower comfort.
User comfort ranking results by model.
| Rank | Model | Acceleration Square (m2/s4) |
|---|---|---|
| 1 | N/A (2019) | 0.038 |
| 2 | Tesla (N/A) | 0.042 |
| 3 | Lincoln MKZs (2016) | 0.053 |
| 4 | KGM Rexton (N/A) | 0.069 |
| 5 | Ford S-Max (2018) | 0.079 |
| 6 | Mazda 3 (2019) | 0.159 |
| 7 | Toyota Corolla LE (2020) | 0.163 |
| 8 | BMW X5 (2018) | 0.537 |
Brand and dataset metadata used by the ranking.
| Brand | Model | Year | Dataset | Supplementary Note |
|---|---|---|---|---|
| N/A | N/A | 2019 | Vanderbilt ACC | Unknown commercial SUV (Wang et al., 2019). |
| Tesla | N/A | N/A | Central Ohio ACC | The tested Tesla has been retrofitted (Xia et al., 2023). |
| Lincoln | MKZs | 2016 | CATS ACC | - |
| KGM | Rexton | N/A | OpenACC-Casale | - |
| Ford | S-Max | 2018 | OpenACC Vicolungo | - |
| Mazda | 3 | 2019 | OpenACC Asta | - |
| Toyota | Corolla LE | 2020 | MicroSimACC | - |
| BMW | X5 | 2018 | OpenACC ZalaZone | - |
References
- Xia, X., Meng, Z., Han, X., Li, H., Tsukiji, T., Xu, R., Zheng, Z., Ma, J., 2023. An automated driving systems data acquisition and analytics platform. Transportation Research Part C: Emerging Technologies 151, 104120.
- Wang, Y., Gunter, G., Nice, M., Work, D.B., 2019. Estimating adaptive cruise control model parameters from on-board radar units. arXiv preprint arXiv:1911.06454.
Energy-efficiency Ranking
Energy-efficiency is measured by fuel consumption rate. Lower fuel consumption indicates better energy-efficiency performance.
Energy-efficiency ranking results by model. Fuel consumption is estimated using the VT-Micro model (Zegeye et al., 2013).
| Rank | Model | Fuel Consumption (L/s) |
|---|---|---|
| 1 | Tesla (N/A) | 0.00220 |
| 2 | KGM Rexton (N/A) | 0.00298 |
| 3 | Lincoln MKZs (2016) | 0.00300 |
| 4 | Ford S-Max (2018) | 0.00314 |
| 5 | N/A (2019) | 0.00318 |
| 6 | Toyota Corolla LE (2020) | 0.00326 |
| 7 | Mazda 3 (2019) | 0.00362 |
| 8 | BMW X5 (2018) | 0.01123 |
Brand and dataset metadata used by the ranking.
| Brand | Model | Year | Dataset | Supplementary Note |
|---|---|---|---|---|
| Tesla | N/A | N/A | Central Ohio ACC | The tested Tesla has been retrofitted (Xia et al., 2023). |
| KGM | Rexton | N/A | OpenACC-Casale | - |
| Lincoln | MKZs | 2016 | CATS ACC | - |
| Ford | S-Max | 2018 | OpenACC Vicolungo | - |
| N/A | N/A | 2019 | Vanderbilt ACC | Unknown commercial SUV (Wang et al., 2019). |
| Toyota | Corolla LE | 2020 | MicroSimACC | - |
| Mazda | 3 | 2019 | OpenACC Asta | - |
| BMW | X5 | 2018 | OpenACC ZalaZone | - |
References
- Xia, X., Meng, Z., Han, X., Li, H., Tsukiji, T., Xu, R., Zheng, Z., Ma, J., 2023. An automated driving systems data acquisition and analytics platform. Transportation Research Part C: Emerging Technologies 151, 104120.
- Wang, Y., Gunter, G., Nice, M., Work, D.B., 2019. Estimating adaptive cruise control model parameters from on-board radar units. arXiv preprint arXiv:1911.06454.
- Zegeye, S. K., De Schutter, B., Hellendoorn, J., Breunesse, E. A., Hegyi, A., 2013. Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes. Transportation Research Part C: Emerging Technologies 31, 158-171.
Submit a Result
For results submissions, please contact technical contributor Hang Zhou (hzhou364@wisc.edu).