Formal Models for Intelligent Speed Validation and Adaptation

Spichkova, Maria; Simić, Milan; Schmidt, Heinz; Cheng, Jian; Dong, Xuening; Gui, Yuxi; Liang, Yin; Ling, Peng; Yin, Zixiong

doi:10.1016/j.procs.2016.08.208

Cited by 8 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the earliest works [7] exploits GPS localization and a given map defining the environment's risk levels. Another study [8] assesses the risk Fig. 2.…”

Section: Related Workmentioning

confidence: 99%

“…1) when the network file is ready to be read, i.e. the DQL core is not writing on the file, the DQL middleware loads the weights of the network and the value of ε from the network file, and the simulator starts a new episode; 2) the simulator sends a message to the controller containing the current scenario specifications; 3) the DQL middleware processes the message to identify the current state s t and collects the reward r t −1 that refers to the state change from s t −1 to s t caused by the past action a t −1 ; then, it composes a new tuple s t −1 , a t −1 , r t −1 , s t ; 4) if the episode fails, the DQL middleware saves the tuple in RB rare, the simulation ends and the process restarts from step 1); otherwise, it saves the tuple in RB common; 5) the DQL middleware calls the network with the current state s t to estimate the rewards of all possible actions and chooses the next a t using the ε-greedy policy; 6) the DQL middleware computes the new value of RCS, v using equation (8) and sends it to the ADS. 7) the process restarts from step 1) until the training is completed.…”

Section: A the Training Proceduresmentioning

confidence: 99%

“…The only adjustment we made to use the network on the real vehicle (driven by the same agent) was the value of v in equation (8), which was decreased at 0.035 m/s to prevent excessive oscillations in the system in the gear shifts. The problem is caused by the vehicle's low-level driveline control, which regulates the automatic transmission: the control is too slow to actuate the updated value of the RCS so that this delay excites the system and generates oscillations.…”

Section: Test On the Real Vehiclementioning

confidence: 99%

See 2 more Smart Citations

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Papini

Plebe

Lio

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Driving requires the ability to handle unpredictable situations. Since it is not always possible to predict an impending danger, a good driver should preventively assess whether a situation has risks and adopt a safe behavior. Considering, in particular, the possibility of a pedestrian suddenly crossing the road, a prudent driver should limit the traveling speed. We present a work exploiting reinforcement learning to learn a function that specifies the safe speed limit for a given artificial driver agent. The safe speed function acts as a behavioral directive for the agent, thus extending its cognitive abilities. We consider scenarios where the vehicle interacts with a distracted pedestrian that might cross the road in hard-to-predict ways and propose a neural network mapping the pedestrian's context onto the appropriate traveling speed so that the autonomous vehicle can successfully perform emergency braking maneuvers. We discuss the advantages of developing a specialized neural network extension on top of an already functioning autonomous driving system, removing the burden of learning to drive from scratch while focusing on learning safe behavior at a highlevel. We demonstrate how the safe speed function can be learned in simulation and then transferred into a real vehicle. We include a statistical analysis of the network's improvements compared to the original autonomous driving system.

show abstract

“…One of the earliest works [7] exploits GPS localization and a given map defining the environment's risk levels. Another study [8] assesses the risk Fig. 2.…”

Section: Related Workmentioning

confidence: 99%

Section: A the Training Proceduresmentioning

confidence: 99%

Section: Test On the Real Vehiclementioning

confidence: 99%

See 1 more Smart Citation

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Papini

Plebe

Lio

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…For this reason, speed limit databases up to date with possible variations (time of the day, weather conditions, type of vehicle) are necessary. An approach to develop and use a speed limit database in the ISA warning system was implemented as a mobile application software for Australian roads [ 23 ].…”

Section: Literature Overviewmentioning

confidence: 99%

“…Speed limits vary between countries, types of roads, areas, vehicles and inclusive timing [ 23 ]. They may also be affected by certain circumstances like construction or special events around a zone (i.e., fairs, carnivals, street markets, etc.).…”

Section: Dynamic Speed Adaptationmentioning

confidence: 99%

Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits

Serna

Ruichek

2017

Sensors

View full text Add to dashboard Cite

A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle’s speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, ‘curve analysis extraction’ and ‘speed limits database creation’ are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger.

show abstract

Software Development for Autonomous and Social Robotics Systems

Sun

Zhang

Liu

et al. 2018

Intelligent Interactive Multimedia Systems and Services

Self Cite

View full text Add to dashboard Cite

In this paper we introduce the core results of the project on software development for social robotics systems. The usability of maintenance and control features is crucial for many kinds of systems, but in the case of social robotics we also have to take into account that (1) the humanoid robot physically interacts with humans, (2) the conversation with children might have different requirements in comparison to the conversation with adults. The results of our work were implement for the humanoid PAL REEM robot, but their core ideas can be applied for other types of humanoid robots. We developed a web-based solution that supports the management of robot-guided tours, provides recommendations for the users as well as allows for a visual analysis of the data on previous tours.

show abstract

Formal Models for Intelligent Speed Validation and Adaptation

Cited by 8 publications

References 15 publications

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits

Software Development for Autonomous and Social Robotics Systems

Contact Info

Product

Resources

About