# Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Physics-Guided Reinforcement Learning for Adaptive Cruise Control

#### 3.1. Soft Actor-Critic Algorithm

#### 3.2. Prior Knowledge

#### 3.3. Integration of Prior Knowledge

## 4. Simulation

#### 4.1. Leading Agent Acceleration

- Random acceleration at each time step (randomAcc),
- Constant acceleration with random full stops (setting lead velocity with $v=0$) (randomStops9 accelerates by 90% of its capacity and randomStops10 accelerates full throttle)
- Predetermined acceleration for each time step (predAcc).

#### 4.2. States

#### 4.3. Penalization

#### 4.4. Reward

#### 4.5. Termination Conditions

#### 4.6. Parameter Search Test

#### 4.7. Perturbed Inputs

#### 4.8. Training Setup

## 5. Evaluation

#### 5.1. Task 1

#### 5.2. Task 2

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations and Nomenclature

ACC | Adaptive Cruise Control |

ML | Machine Learning |

SAC | Soft Actor-Critic Algorithm |

RL | Reinforcement Learning |

PG | Physics-guided |

AI | Artificial Intelligence |

AV | Autonomous vehicle |

MRI | Magnetic Resonance Imaging |

LIDAR | Light detection and ranging |

IDM | Intelligent Driver Model |

MDP | Markov decision process |

HW | Headway |

THW | Time Headway |

DST | Deceleration to Safety Time |

${Q}_{\theta}({s}_{t},{a}_{t})$ | Soft Q-function |

${s}_{t}$ | State at time point t |

${a}_{t}$ | Action at time point t |

$\theta $ | Soft Q-function parameter |

${\pi}_{\varphi}\left({s}_{t}\right|{a}_{t})$ | Policy with state-action pair |

$\varphi $ | Policy parameter |

${v}_{0}$ | Desired velocity |

T | Save time headway |

${a}_{m}$ | Maximum acceleration |

b | Desired deceleration |

${s}_{0}$ | Jam distance |

s | Distance to the leading vehicle |

${s}^{*}$ | Minimum jam-avoiding distance |

v | Current agent velocity |

$\Delta v$ | Velocity difference to the leading vehicle |

t | Time point t |

${v}_{t}$ | Velocity at time point t |

${a}_{t}$ | Acceleration at time point t |

${x}_{t}$ | Position at time point t |

h | Step size |

$\mathcal{S}$ | State Space in MDP |

$\mathcal{A}$ | Action Space in MDP |

T | Deterministic transition model in MDP |

r | Reward |

${\pi}_{\varphi}:\mathcal{S}\to \mathcal{A}$ | Deterministic parametric policy |

$ts$ | Target seperation |

$\mathrm{DST}(v1,v2,s,ts)$ | Deceleration to Safety Time |

$v1$ | Vehicle 1 |

$v2$ | Vehicle 2 |

## Appendix A

Model | Collisions | HW (m) | THW (s) | Separation (m) | |
---|---|---|---|---|---|

0 | 0/predAcc/symmetric | 2 | 21.986616 | 4.746649 | 17.932934 |

1 | 0/predAcc/symmetric | 20 | 21.523313 | 4.598207 | 17.974341 |

2 | 0/predAcc/velocity | 20 | 12.238809 | 6.684551 | 17.714547 |

3 | 0/predAcc/absoluteDiff | 20 | 13.512952 | 7.932466 | 18.123747 |

4 | 0/predAcc/None | 10 | 552.604185 | 22.768953 | 21.750825 |

5 | 0/randomAcc/symmetric | 0 | 120.883542 | 5.407901 | 147.354975 |

6 | 0/randomAcc/velocity | 20 | 12.467875 | 7.977922 | 17.749884 |

7 | 0/randomAcc/absoluteDiff | 0 | 185.853298 | 8.008320 | 171.210850 |

8 | 0/randomAcc/None | 20 | 30.555453 | 4.365431 | 49.872451 |

9 | 0/randomStops9/symmetric | 10 | 111.509197 | 5.244257 | 17.899641 |

10 | 0/randomStops9/velocity | 20 | 17.324510 | 3.036292 | 13.902887 |

11 | 0/randomStops9/absoluteDiff | 20 | 13.116112 | 7.374774 | 17.958673 |

12 | 0/randomStops9/None | 0 | 818.533661 | 32.071159 | 229.345633 |

13 | 0/randomStops10/symmetric | 20 | 11.578542 | 2.877303 | 19.522080 |

14 | 0/randomStops10/velocity | 0 | 96.373722 | 4.650809 | 103.114341 |

15 | 0/randomStops10/absoluteDiff | 20 | 13.114346 | 7.399617 | 17.963344 |

16 | 0/randomStops10/None | 0 | 818.434954 | 32.064495 | 229.345483 |

17 | 100/predAcc/symmetric | 20 | 64.280247 | 7.214039 | 73.357278 |

18 | 100/predAcc/velocity | 20 | 12.276690 | 4.083880 | 13.097846 |

19 | 100/predAcc/absoluteDiff | 20 | 13.453637 | 7.653036 | 18.272531 |

20 | 100/predAcc/None | 10 | 795.550077 | 30.799090 | 22.119458 |

21 | 100/randomAcc/symmetric | 9 | 38.420217 | 2.480810 | 54.983419 |

22 | 100/randomAcc/velocity | 4 | 201.610520 | 7.274791 | 91.056809 |

23 | 100/randomAcc/absoluteDiff | 10 | 85.191944 | 4.217098 | 62.292352 |

24 | 100/randomAcc/None | 0 | 229.362628 | 10.193637 | 213.209962 |

25 | 100/randomStops9/symmetric | 0 | 88.737306 | 3.578192 | 89.672347 |

26 | 100/randomStops9/velocity | 20 | 9.988424 | 2.657730 | 13.041972 |

27 | 100/randomStops9/absoluteDiff | 20 | 12.637536 | 7.668094 | 17.778202 |

28 | 100/randomStops9/None | 0 | 794.717912 | 31.126503 | 229.345636 |

29 | 100/randomStops10/symmetric | 2 | 119.310468 | 5.001601 | 95.460083 |

30 | 100/randomStops10/velocity | 20 | 26.619944 | 4.218386 | 53.601016 |

31 | 100/randomStops10/absoluteDiff | 20 | 40.782619 | 4.782894 | 21.429448 |

32 | 100/randomStops10/None | 0 | 815.799968 | 31.938171 | 229.311862 |

33 | 3,000/predAcc/symmetric | 0 | 30.806897 | 2.256980 | 104.479287 |

34 | 3,000/predAcc/velocity | 18 | 16.539250 | 2.489905 | 10.804999 |

35 | 3,000/predAcc/absoluteDiff | 20 | 13.571941 | 4.840553 | 18.439911 |

36 | 3,000/predAcc/None | 0 | 800.393554 | 31.542861 | 229.345636 |

37 | 3,000/randomAcc/symmetric | 0 | 557.436259 | 21.370929 | 189.098820 |

38 | 3,000/randomAcc/velocity | 1 | 314.244283 | 11.907776 | 174.433635 |

39 | 3,000/randomAcc/absoluteDiff | 0 | 519.028676 | 19.635769 | 198.585035 |

40 | 3,000/randomAcc/None | 0 | 487.673884 | 18.984621 | 227.800876 |

41 | 3,000/randomStops9/symmetric | 0 | 121.610496 | 5.503739 | 145.529442 |

42 | 3,000/randomStops9/velocity | 0 | 71.099924 | 3.601645 | 96.712122 |

43 | 3,000/randomStops9/absoluteDiff | 20 | 12.240173 | 2.740492 | 16.209725 |

44 | 3,000/randomStops9/None | 0 | 736.721510 | 29.327481 | 229.345636 |

45 | 3,000/randomStops10/symmetric | 0 | 95.886739 | 4.443669 | 103.119817 |

46 | 3,000/randomStops10/velocity | 14 | 9.988159 | 4.999919 | 33.352000 |

47 | 3,000/randomStops10/absoluteDiff | 20 | 13.616155 | 6.805060 | 18.093497 |

48 | 3,000/randomStops10/None | 0 | 160.721774 | 7.162411 | 154.905059 |

49 | 100,000/predAcc/symmetric | 16 | 76.071190 | 3.418030 | 16.016787 |

50 | 100,000/predAcc/velocity | 1 | 134.614581 | 6.869882 | 149.200815 |

51 | 100,000/predAcc/absoluteDiff | 0 | 818.685845 | 32.077105 | 229.344549 |

52 | 100,000/predAcc/None | 0 | 761.794150 | 29.485078 | 229.345636 |

53 | 100,000/randomAcc/symmetric | 0 | 160.713442 | 8.460585 | 225.571123 |

54 | 100,000/randomAcc/velocity | 0 | 817.021321 | 31.972462 | 229.345213 |

55 | 100,000/randomAcc/absoluteDiff | 0 | 576.845622 | 23.913704 | 229.345636 |

56 | 100,000/randomAcc/None | 0 | 509.216830 | 21.192002 | 229.345242 |

57 | 100,000/randomStops9/symmetric | 0 | 90.975791 | 4.659583 | 135.021949 |

58 | 100,000/randomStops9/velocity | 0 | 76.346305 | 4.586848 | 130.914674 |

59 | 100,000/randomStops9/absoluteDiff | 0 | 292.012632 | 14.117718 | 229.345634 |

60 | 100,000/randomStops9/None | 0 | 816.244478 | 31.961323 | 229.345221 |

61 | 100,000/randomStops10/symmetric | 0 | 174.359445 | 7.620220 | 144.156391 |

62 | 100,000/randomStops10/velocity | 0 | 430.033252 | 18.551971 | 229.345570 |

63 | 100,000/randomStops10/absoluteDiff | 0 | 379.953210 | 16.932130 | 229.340859 |

64 | 100,000/randomStops10/None | 10 | 688.564158 | 26.086424 | 54.548802 |

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**Figure 1.**Proposed PG RL approach for increasing the safety of ACC by integrating prior knowledge in the form of the Jam-Avoiding Distance.

**Figure 2.**Graph of the reward function for different values of s at $\mathrm{ts}=10\mathrm{m}$. The y-axis is representing the reward value, while the x-axis is representing the number of simulation steps.

**Figure 3.**Graph of the finals positions after the first task using the traditional RL (blue color) and the proposed PG RL (red color) models. The y-axis is representing the position behind the lead vehicle in meters while one point on the x-axis is referring to exactly one vehicle. These are the average final positions at the end of the scenarios, with the numbers referring to the vehicles (from back to front).

**Figure 4.**Graph of the finals positions after the first task with randomized inputs using the traditional RL (blue color) and the proposed PG RL (red color) models. The y-axis is representing the position behind the lead vehicle in meters while one point on the x-axis is referring to exactly one vehicle. These are the average final positions at the end of the scenarios, with the numbers referring to the vehicles (from back to front).

**Figure 5.**Graph of the finals positions after the first task with randomized inputs using the traditional RL (blue color) and the proposed PG RL (red color) models trained with perturbations. The y-axis is representing the position behind the lead vehicle in meters while one point on the x-axis is referring to exactly one vehicle. These are the average final positions at the end of the scenarios, with the numbers referring to the vehicles (from back to front).

**Figure 6.**HW values at each step for both models (traditional RL in blue color line; our proposed PG RL model in red color line). The y-axis is representing the HW values while the x-axis represents the number of simulation steps.

**Figure 7.**THW values at each step for both models (traditional RL in blue color line; our proposed PG RL model in red color line). The y-axis is representing the THW values while the x-axis represents the number of simulation steps.

**Figure 8.**DST values at each step for both models (traditional RL in blue color line; our proposed PG RL model in red color line). The y-axis is representing the DST while the x-axis represents the number of simulation steps.

**Table 1.**Static model parameters used in the proposed approach for increasing the safety of ACC [14].

Static Model Parameter | Symbol | Value |
---|---|---|

Desired velocity | ${v}_{0}$ | 120 $\mathrm{k}$$\mathrm{m}$/$\mathrm{h}$ |

Save time headway | T | $1.5$$\mathrm{s}$ |

Maximum acceleration | ${a}_{m}$ | $1.0$$\mathrm{m}$/$\mathrm{s}$${}^{2}$ |

Desired deceleration | b | $2.0$$\mathrm{m}$/$\mathrm{s}$${}^{2}$ |

Jam distance | ${s}_{0}$ | 2 $\mathrm{m}$ |

Lead Deceleration | Collision Vehicle PG RL | Collision Vehicle RL |
---|---|---|

1.0 | 10th | 1st |

0.75 | No collisions | 1st |

0.71 | No collisions | 6th |

0.7 | No collisions | No collisions |

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## Share and Cite

**MDPI and ACS Style**

Jurj, S.L.; Grundt, D.; Werner, T.; Borchers, P.; Rothemann, K.; Möhlmann, E. Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning. *Energies* **2021**, *14*, 7572.
https://doi.org/10.3390/en14227572

**AMA Style**

Jurj SL, Grundt D, Werner T, Borchers P, Rothemann K, Möhlmann E. Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning. *Energies*. 2021; 14(22):7572.
https://doi.org/10.3390/en14227572

**Chicago/Turabian Style**

Jurj, Sorin Liviu, Dominik Grundt, Tino Werner, Philipp Borchers, Karina Rothemann, and Eike Möhlmann. 2021. "Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning" *Energies* 14, no. 22: 7572.
https://doi.org/10.3390/en14227572