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Apart from that, there are Stop & Go systems that support lower velocites (e.g. < 40 km/h). In our case, both systems might be needed and it might be reasonable to develop two different systems for ACC and Stop & Go. +There are basically three different techniques that can be used to implement an ACC: PID Control, Model Predictive Control and Fuzzy Logic Control. Another option is CACC (Cooperative Adaptive Cruise Control) but this is not relevant for our project since it requires communication between the vehicles. + +### PID Control + +### Model Predictive Control (MPC) +It calculates the current control action by solving an online, iterative and finite-horizon optimization of the model. +Procedure: +1. Prediction of future system states based on current states +2. Computation of the cost function for a finite time horizon in the future +3. Implementation of the first step of the solved control sequence +4. Application of the feedback control loop to compensate for the predictive error and model inaccuracy +5. Sampling of new current states and repitition of the process + +### Fuzzy Logic Control (FLC) +Provides a unified control framework to offer both functions: ACC and Stop & Go. + + +![Example of a FLC control algorithm](../../../assets/research_assets/ACC_FLC_Example_1.png) + +## Discussion + +- When should the ACC be used? Only when driving straight forward? +- How to test adaptations of the ACC? Create test scenarios? +- Which output should be transfered to the Acting component? Only the desired speed? +- Which input do we get from the Perception component? The distance and velocity of the car in front? + +## ACC Algorithm + + + + +## Sources +https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review \ No newline at end of file From ab7a8669a5e59c31dbba600438a05514239c5e1b Mon Sep 17 00:00:00 2001 From: seefelke <33551476+seefelke@users.noreply.github.com> Date: Fri, 22 Nov 2024 01:17:55 +0100 Subject: [PATCH 02/11] Update ACC.md added information about current implementation, new concept and next steps --- doc/research/paf24/planning/ACC.md | 49 +++++++++++++++++++++++++++++- 1 file changed, 48 insertions(+), 1 deletion(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index 7d17d2f1..c319c4fe 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -31,8 +31,55 @@ Provides a unified control framework to offer both functions: ACC and Stop & Go. ## ACC Algorithm +### Current implementation + +- General behaviour: + checks for obstacle distance < safety distance + + calculates new speed based on obstacle distance + + else keep current speed + +- publishes speed to acc_velocity +- safe distance calculation is currently not correct, uses speed + (speed * 0.36)² which results in wrong distances +- if car in front of us ignores speed limits we ignore them as well +- some parts of unstuck routine are in ACC and need to be refactored +- same goes for publishing of current waypoint, this should not be in ACC + +In summary, the current implementation is not sufficient and needs major refactoring. + +### Concept for new implementation + +The new concept for the ACC is to take the trajectory, look at all or a limited subset of the next points and add a target velocity and the current behaviour to each point. +This way the Acting has more knowledge about what the car is doing and can adjust accordingly in a local manner. +For this a new trajectory message type was implemented in #511. + +![trajectoryMsg](https://github.com/user-attachments/assets/0b452f1a-4c60-45b2-882f-3a50118c9cb9) + +Since behaviour is passed as an ID a new enum for behaviours was implemented in utils.py as well. + +The general idea for speeds above the 40 km/h mark is to calculate a proper safety distance and a general target velocity. For speeds lower than that a stop and go system needs to be discussed. + +For safety distance the old approach can simply be modified. For example, (speed / 10)*3 + (speed / 10)² is a well known formula for calculating the braking distance. + +For a general speed target we need to take the speed and the speed of the car in front into account. In cases where the car in front is substantially slower than the speed limit ACC could inititate overtaking. + +With the general speed target and the current distance to the car in front we can calculate the target velocity for each point in the trajectory up to the car in front for example by interpolation. Points further that that will be inititalized with the speed limit at that position. + +### Possible next steps +A seperate file for the new ACC should be created to not disturb the system. + +The parts that might get cut from ACC like current waypoint and unstuck routine need to be evaluated for necessity and if need be moved to somewhere more fitting. + +Implement publisher for new message type. + +Start implementing safety distance and general target speed logic. Subscriber logic could be taken from old implementation. + +### Requirements: +- obstacle speed +- obstacle distance ## Sources -https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review \ No newline at end of file +https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review From 1b3ea9a2c75c4d2316b19b5b0ac2ad03e3ca407b Mon Sep 17 00:00:00 2001 From: seefelke <33551476+seefelke@users.noreply.github.com> Date: Fri, 22 Nov 2024 13:08:07 +0100 Subject: [PATCH 03/11] Update ACC.md fix linter --- doc/research/paf24/planning/ACC.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index c319c4fe..1a3474f3 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -1,6 +1,7 @@ # ACC ## General + The main goal of an ACC (Adaptive Cruise Control) is to follow a car driving in front while keeping a safe distance to it. This can be achieved by adjusting the speed to the speed of the car in front. In general, classic ACC systems are designed for higher velocity (e.g. > 40 km/h). Apart from that, there are Stop & Go systems that support lower velocites (e.g. < 40 km/h). In our case, both systems might be needed and it might be reasonable to develop two different systems for ACC and Stop & Go. There are basically three different techniques that can be used to implement an ACC: PID Control, Model Predictive Control and Fuzzy Logic Control. Another option is CACC (Cooperative Adaptive Cruise Control) but this is not relevant for our project since it requires communication between the vehicles. @@ -8,8 +9,10 @@ There are basically three different techniques that can be used to implement an ### PID Control ### Model Predictive Control (MPC) + It calculates the current control action by solving an online, iterative and finite-horizon optimization of the model. Procedure: + 1. Prediction of future system states based on current states 2. Computation of the cost function for a finite time horizon in the future 3. Implementation of the first step of the solved control sequence @@ -17,8 +20,8 @@ Procedure: 5. Sampling of new current states and repitition of the process ### Fuzzy Logic Control (FLC) -Provides a unified control framework to offer both functions: ACC and Stop & Go. +Provides a unified control framework to offer both functions: ACC and Stop & Go. ![Example of a FLC control algorithm](../../../assets/research_assets/ACC_FLC_Example_1.png) @@ -67,6 +70,7 @@ For a general speed target we need to take the speed and the speed of the car in With the general speed target and the current distance to the car in front we can calculate the target velocity for each point in the trajectory up to the car in front for example by interpolation. Points further that that will be inititalized with the speed limit at that position. ### Possible next steps + A seperate file for the new ACC should be created to not disturb the system. The parts that might get cut from ACC like current waypoint and unstuck routine need to be evaluated for necessity and if need be moved to somewhere more fitting. @@ -75,11 +79,11 @@ Implement publisher for new message type. Start implementing safety distance and general target speed logic. Subscriber logic could be taken from old implementation. -### Requirements: +### Requirements + - obstacle speed - obstacle distance - - ## Sources -https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review + +[ACC](https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review) From 5dbf85d356e39fae63ac65dc87f578783e44e692 Mon Sep 17 00:00:00 2001 From: Veronika Neumann Date: Fri, 22 Nov 2024 15:45:20 +0100 Subject: [PATCH 04/11] PID Control added --- doc/research/paf24/planning/ACC.md | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index 1a3474f3..ac8a8747 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -1,5 +1,7 @@ # ACC +This file gives a general overview on possible ACC implementations and reflects the current state of the ACC. + ## General The main goal of an ACC (Adaptive Cruise Control) is to follow a car driving in front while keeping a safe distance to it. This can be achieved by adjusting the speed to the speed of the car in front. @@ -8,6 +10,15 @@ There are basically three different techniques that can be used to implement an ### PID Control +The PID Controller consists of three terms: the proportional term, the integral term and the derivative term. One possible simple controller model looks as follows: + +$$ v_f(t) = v_f(t - t_s) + k_p e (t-t_s) + k_d \dot{e}(t - t_s) $$ +$$ e(t-t_s) = \Delta x(t - t_s) - t_{hw,d} v_f (t - t_s) $$ + +- $t_s$: sampling time +- $k_p$ and $k_d$: coefficients for proportional and derivative terms +- $e$: distance error (difference between actual distance $\Delta x$ and desired distance $\Delta x_d$) + ### Model Predictive Control (MPC) It calculates the current control action by solving an online, iterative and finite-horizon optimization of the model. @@ -32,7 +43,7 @@ Provides a unified control framework to offer both functions: ACC and Stop & Go. - Which output should be transfered to the Acting component? Only the desired speed? - Which input do we get from the Perception component? The distance and velocity of the car in front? -## ACC Algorithm +## ACC in our project ### Current implementation @@ -86,4 +97,4 @@ Start implementing safety distance and general target speed logic. Subscriber lo ## Sources -[ACC](https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review) +[He, Yinglong et al. (2019). Adaptive Cruise Control Strategies Implemented on Experimental Vehicles: A Review. IFAC-PapersOnLine. 52. 21-27. 10.1016/j.ifacol.2019.09.004. ](https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review) From dbedea443d30ff6f77f049a2f756804206220125 Mon Sep 17 00:00:00 2001 From: Veronika Neumann Date: Fri, 22 Nov 2024 15:58:25 +0100 Subject: [PATCH 05/11] Link fixed --- doc/research/paf24/planning/ACC.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index ac8a8747..83cd2949 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -97,4 +97,5 @@ Start implementing safety distance and general target speed logic. Subscriber lo ## Sources -[He, Yinglong et al. (2019). Adaptive Cruise Control Strategies Implemented on Experimental Vehicles: A Review. IFAC-PapersOnLine. 52. 21-27. 10.1016/j.ifacol.2019.09.004. ](https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review) +He, Yinglong et al. (2019). Adaptive Cruise Control Strategies Implemented on Experimental Vehicles: A Review. IFAC-PapersOnLine. 52. 21-27. 10.1016/j.ifacol.2019.09.004. +[Link](https://www.researchgate.net/publication/335934496_Adaptive_Cruise_Control_Strategies_Implemented_on_Experimental_Vehicles_A_Review) From 11e94fd9bd63562b885d051fb8ec6dd0e7006cdd Mon Sep 17 00:00:00 2001 From: seefelke <33551476+seefelke@users.noreply.github.com> Date: Sun, 24 Nov 2024 01:44:40 +0100 Subject: [PATCH 06/11] Update ACC.md made proposed concept more detailed --- doc/research/paf24/planning/ACC.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index 83cd2949..aeac45f4 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -72,13 +72,19 @@ For this a new trajectory message type was implemented in #511. Since behaviour is passed as an ID a new enum for behaviours was implemented in utils.py as well. -The general idea for speeds above the 40 km/h mark is to calculate a proper safety distance and a general target velocity. For speeds lower than that a stop and go system needs to be discussed. +The general idea for speeds above the 40 km/h mark is to calculate a proper safety distance, a general target velocity and velocity targets based on PID. For speeds lower than that a stop and go system can be discussed if it is really needed. -For safety distance the old approach can simply be modified. For example, (speed / 10)*3 + (speed / 10)² is a well known formula for calculating the braking distance. +For safety distance we will use the concept from the FLC graphic. -For a general speed target we need to take the speed and the speed of the car in front into account. In cases where the car in front is substantially slower than the speed limit ACC could inititate overtaking. +For a general speed target we either take the speed of the car in front or the speed limit, whichever is lower. In cases where the car in front is substantially slower than the speed limit ACC could inititate overtaking. -With the general speed target and the current distance to the car in front we can calculate the target velocity for each point in the trajectory up to the car in front for example by interpolation. Points further that that will be inititalized with the speed limit at that position. +Since we want to calculate the desired speed at each point of the trajectory, the way PID calculates velocity seems reasonable since we can treat the trajectory points as different points in time for the sampling time. For example let's say we sample every fifth point and calculate the velocity for that, then we can just interpolate every other point inbetween. + +$v_f(t - t_s)$ would then simply be the velocity of the fifth point before the current one. Theoretically this allows us to dynamically adjust the sampling time as well if needed. + +For the distance error we can use the safety distance as the desired distance. The distance at time t needs to be predicted based on the (predicted) distance at the prior sample point and the calculated speed at the prior sample point. + +We calculate velocities like that up to the point where the velocity reaches the desired general speed target. For points further than that we simply use the desired general speed. ### Possible next steps @@ -90,6 +96,8 @@ Implement publisher for new message type. Start implementing safety distance and general target speed logic. Subscriber logic could be taken from old implementation. +Implement PID logic + ### Requirements - obstacle speed From f55db3ffcd0ef7887a699991e98ca2dc3d2d0796 Mon Sep 17 00:00:00 2001 From: seefelke <33551476+seefelke@users.noreply.github.com> Date: Sun, 24 Nov 2024 01:47:33 +0100 Subject: [PATCH 07/11] Update ACC.md linter --- doc/research/paf24/planning/ACC.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index aeac45f4..e99b1a0c 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -78,11 +78,12 @@ For safety distance we will use the concept from the FLC graphic. For a general speed target we either take the speed of the car in front or the speed limit, whichever is lower. In cases where the car in front is substantially slower than the speed limit ACC could inititate overtaking. -Since we want to calculate the desired speed at each point of the trajectory, the way PID calculates velocity seems reasonable since we can treat the trajectory points as different points in time for the sampling time. For example let's say we sample every fifth point and calculate the velocity for that, then we can just interpolate every other point inbetween. +Since we want to calculate the desired speed at each point of the trajectory, the way PID calculates velocity seems reasonable since we can treat the trajectory points as different points in time for the sampling time. +For example let's say we sample every fifth point and calculate the velocity for that, then we can just interpolate every other point inbetween. $v_f(t - t_s)$ would then simply be the velocity of the fifth point before the current one. Theoretically this allows us to dynamically adjust the sampling time as well if needed. -For the distance error we can use the safety distance as the desired distance. The distance at time t needs to be predicted based on the (predicted) distance at the prior sample point and the calculated speed at the prior sample point. +For the distance error we can use the safety distance as the desired distance. The distance at time t needs to be predicted based on the (predicted) distance at the prior sample point and the calculated speed at the prior sample point. We calculate velocities like that up to the point where the velocity reaches the desired general speed target. For points further than that we simply use the desired general speed. From d93d240544b98b651cfb80b005c9478f6519f975 Mon Sep 17 00:00:00 2001 From: seefelke <33551476+seefelke@users.noreply.github.com> Date: Sun, 24 Nov 2024 02:04:46 +0100 Subject: [PATCH 08/11] Update ACC.md --- doc/research/paf24/planning/ACC.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index e99b1a0c..218da37c 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -74,7 +74,8 @@ Since behaviour is passed as an ID a new enum for behaviours was implemented in The general idea for speeds above the 40 km/h mark is to calculate a proper safety distance, a general target velocity and velocity targets based on PID. For speeds lower than that a stop and go system can be discussed if it is really needed. -For safety distance we will use the concept from the FLC graphic. +For safety distance we would like to calculate it like FLC but that most likely needs some adjustments as setting the distance to 100m when there is no vehicle ahead seems unreasonable. +We can just directly set the velocity to the speed limit in that case. For a general speed target we either take the speed of the car in front or the speed limit, whichever is lower. In cases where the car in front is substantially slower than the speed limit ACC could inititate overtaking. From 9b3870af0f556c739d8b52097cdb85dfa39bee7d Mon Sep 17 00:00:00 2001 From: seefelke <33551476+seefelke@users.noreply.github.com> Date: Mon, 25 Nov 2024 02:46:24 +0100 Subject: [PATCH 09/11] Update ACC.md added diagram for presentation --- doc/research/paf24/planning/ACC.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index 218da37c..4a8b0295 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -86,7 +86,9 @@ $v_f(t - t_s)$ would then simply be the velocity of the fifth point before the c For the distance error we can use the safety distance as the desired distance. The distance at time t needs to be predicted based on the (predicted) distance at the prior sample point and the calculated speed at the prior sample point. -We calculate velocities like that up to the point where the velocity reaches the desired general speed target. For points further than that we simply use the desired general speed. +We calculate velocities like that up to the point where the actual distance is within 5% of the optimal safety distance. For points further than that we simply use the desired general speed. + +![accDiagram](https://github.com/user-attachments/assets/9a7b4572-f041-4da0-900c-51ab20d1904b) ### Possible next steps From 6b75683370c005ff8e8ad7f0e3ca27a41b13c6bf Mon Sep 17 00:00:00 2001 From: Veronika Neumann Date: Mon, 25 Nov 2024 09:24:44 +0100 Subject: [PATCH 10/11] Integrated review feedback --- doc/research/paf24/planning/ACC.md | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index 4a8b0295..9c1da07e 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -6,18 +6,21 @@ This file gives a general overview on possible ACC implementations and reflects The main goal of an ACC (Adaptive Cruise Control) is to follow a car driving in front while keeping a safe distance to it. This can be achieved by adjusting the speed to the speed of the car in front. In general, classic ACC systems are designed for higher velocity (e.g. > 40 km/h). Apart from that, there are Stop & Go systems that support lower velocites (e.g. < 40 km/h). In our case, both systems might be needed and it might be reasonable to develop two different systems for ACC and Stop & Go. +The threshold to distinguish between the two systems has to be chosen reasonably. There are basically three different techniques that can be used to implement an ACC: PID Control, Model Predictive Control and Fuzzy Logic Control. Another option is CACC (Cooperative Adaptive Cruise Control) but this is not relevant for our project since it requires communication between the vehicles. ### PID Control The PID Controller consists of three terms: the proportional term, the integral term and the derivative term. One possible simple controller model looks as follows: -$$ v_f(t) = v_f(t - t_s) + k_p e (t-t_s) + k_d \dot{e}(t - t_s) $$ +$$ v_f(t) = v_f(t - t_s) + k_p e(t-t_s) + k_i \int_{0}^{t} e(\tau) d\tau + k_d \dot{e}(t - t_s) $$ $$ e(t-t_s) = \Delta x(t - t_s) - t_{hw,d} v_f (t - t_s) $$ +- $v_f$: follower vehicle velocity (transmitted to the acting component) - $t_s$: sampling time -- $k_p$ and $k_d$: coefficients for proportional and derivative terms +- $k_p$ and $k_i$ and $k_d$: coefficients for proportional, integral and derivative terms - $e$: distance error (difference between actual distance $\Delta x$ and desired distance $\Delta x_d$) +- $t_{hw,d}$: desired time headway (duration between the arrival of the first car at a certain waypoint and the arrival of the following car at the same waypoint) ### Model Predictive Control (MPC) @@ -34,14 +37,7 @@ Procedure: Provides a unified control framework to offer both functions: ACC and Stop & Go. -![Example of a FLC control algorithm](../../../assets/research_assets/ACC_FLC_Example_1.png) - -## Discussion - -- When should the ACC be used? Only when driving straight forward? -- How to test adaptations of the ACC? Create test scenarios? -- Which output should be transfered to the Acting component? Only the desired speed? -- Which input do we get from the Perception component? The distance and velocity of the car in front? +![Example of a FLC control algorithm showing input variables (relative distance, host vehicle speed), fuzzy rules processing, and output variable](../../../assets/research_assets/ACC_FLC_Example_1.png) ## ACC in our project @@ -107,6 +103,12 @@ Implement PID logic - obstacle speed - obstacle distance +## Discussion + +- How to test adaptations of the ACC? (Suggestion: Create test scenarios which represent different situations like driving straight forward behing a leading vehicle, no leading vehicle, sudden breaking, etc.) +- Which output should be transfered to the Acting component? (Suggestion: The desired speed is published in the new trajectory. No acceleration data is needed, the acceleration is handled by the acting component.) +- Which input do we get from the Perception component? (Suggestion: The distance and velocity of the car in front would be really helpful.) + ## Sources He, Yinglong et al. (2019). Adaptive Cruise Control Strategies Implemented on Experimental Vehicles: A Review. IFAC-PapersOnLine. 52. 21-27. 10.1016/j.ifacol.2019.09.004. From 93284591fd5f2224a1d5955f80453a36f132fc5d Mon Sep 17 00:00:00 2001 From: Veronika Neumann Date: Mon, 25 Nov 2024 09:26:40 +0100 Subject: [PATCH 11/11] Edited graphics link --- doc/research/paf24/planning/ACC.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/research/paf24/planning/ACC.md b/doc/research/paf24/planning/ACC.md index 9c1da07e..366a02c5 100644 --- a/doc/research/paf24/planning/ACC.md +++ b/doc/research/paf24/planning/ACC.md @@ -37,7 +37,7 @@ Procedure: Provides a unified control framework to offer both functions: ACC and Stop & Go. -![Example of a FLC control algorithm showing input variables (relative distance, host vehicle speed), fuzzy rules processing, and output variable](../../../assets/research_assets/ACC_FLC_Example_1.png) +![Example of a FLC control algorithm showing input variables (relative distance, host vehicle speed), fuzzy rules processing, and output variable](../../../assets/research_assets/ACC_FLC_Example_1.PNG) ## ACC in our project